DEV Community: jelizaveta

Retrieving and Replacing Fonts in Word Documents with Python

jelizaveta — Wed, 15 Jul 2026 01:24:53 +0000

Font management is a recurring challenge when working with Word documents. Whether you need to audit which fonts are used across a file or consistently replace one font with another, manual handling is not only time‑consuming but also error‑prone. By using the Spire.Doc for Python library, you can programmatically inspect and selectively modify font information inside Word documents with just a few lines of code, greatly streamlining the process. This article walks through two concrete examples, explaining the underlying principles and practical tips in detail.

1. Why Choose Spire.Doc for Python

Spire.Doc for Python is a professional‑grade library for Word document manipulation that requires no Microsoft Office installation. It lets you create, modify, convert, and extract content entirely within Python. Its object model closely mirrors Word’s internal structure: a Documentcontains multipleSectionobjects; eachSectionhas aBody; each BodyholdsParagraphelements; and each paragraph consists of child objects such asTextRange, images, and tables. Font attributes—name (FontName), size (FontSize), color (TextColor), and more—are stored in the CharacterFormatproperty of eachTextRange. This hierarchical design provides a clear and logical path for traversing and updating font settings.

2. Scenario 1: Extracting All Font Information from a Document

The first example scans an entire Word document, extracts the font name, size, and color for every text fragment, deduplicates the results, and writes them to a text file. This is especially useful for document consistency reviews and font compliance audits.

from spire.doc import *
from spire.doc.common import *

# Helper to write a list of strings to a text file
def WriteAllText(fname:str, text:List[str]):
    fp = open(fname, "w", encoding="utf-8")
    for s in text:
        fp.write(s)


# Custom class to represent a font identity (name + size)
class FontInfo:
    def __init__(self):
        self._m_name = ''
        self._m_size = None

    def __eq__(self, other):
        if isinstance(other, FontInfo):
            return self._m_name == other.get_name() and self._m_size == other.get_size()
        return False

    def get_name(self):
        return self._m_name

    def set_name(self, value):
        self._m_name = value

    def get_size(self):
        return self._m_size

    def set_size(self, value):
        self._m_size = value


# Prepare variables
fontInformations = ""
font_infos = []

# Load the document
document = Document()
document.LoadFromFile("/sample.docx")

# Iterate over sections, paragraphs, and child objects
for i in range(document.Sections.Count):
    section = document.Sections.get_Item(i)

    for j in range(section.Body.Paragraphs.Count):
        paragraph = section.Body.Paragraphs.get_Item(j)

        for k in range(paragraph.ChildObjects.Count):
            obj = paragraph.ChildObjects.get_Item(k)

            # Only process text ranges
            if isinstance(obj, TextRange):
                txtRange = obj if isinstance(obj, TextRange) else None

                fontName = txtRange.CharacterFormat.FontName
                fontSize = txtRange.CharacterFormat.FontSize
                textColor = txtRange.CharacterFormat.TextColor.Name

                # Deduplicate by name+size (color is not a distinguishing factor for the font itself)
                fontInfo = FontInfo()
                fontInfo.set_name(fontName)
                fontInfo.set_size(fontSize)
                if fontInfo not in font_infos:
                    font_infos.append(fontInfo)                  
                    line = "Font name: {0:s}, Size: {1:f}, Color: {2:s}".format(
                        fontInfo.get_name(), fontInfo.get_size(), textColor
                    )
                    fontInformations += line + '\r'

# Save the results
WriteAllText("/retrieved_fonts.txt", fontInformations)
document.Dispose()

Key Implementation Steps

Load and initialise – document.LoadFromFile("/sample.docx").
Three‑level traversal – loop through Sections, then Paragraphs, then ChildObjects.
Filter for text ranges – use isinstance(obj, TextRange) to isolate elements that carry font formatting.
Extract attributes – from txtRange.CharacterFormat, read FontName (string), FontSize (float, in points), and TextColor.Name (colour name).
Custom deduplication – the FontInfo class overrides __eq__ to treat a combination of name and size as unique. Only new entries are appended to the output string.
Write to file – the WriteAllText helper saves the accumulated string.

Design Considerations

Why deduplicate by name and size only? Colour is not an inherent font property; the same font can appear in multiple colours. Including colour would inflate the results unnecessarily. This approach focuses on the font’s core identity.
Error handling – though not shown, it is wise to guard against FontSize being None (e.g., with WordArt or special styles).
Performance – the nested loops are efficient for typical documents. For files with tables or text boxes, you would need to extend the traversal to those containers as well.

A sample output might look like:

Font name: SimSun, Size: 12.000000, Color: Black
Font name: Arial, Size: 10.500000, Color: Red
...

3. Scenario 2: Targeted Replacement of a Specific Font

The second example is more focused: it replaces every occurrence of “FangSong” (Imitation Song) with “LiSu” (Clerical Script) throughout the document. This is a common task when unifying brand styles or adapting to local typographic conventions.

from spire.doc import *
from spire.doc.common import *

document = Document()
document.LoadFromFile("/sample.docx")

for i in range(document.Sections.Count):
    section = document.Sections.get_Item(i)

    for j in range(section.Body.Paragraphs.Count):
        paragraph = section.Body.Paragraphs.get_Item(j)

        for k in range(paragraph.ChildObjects.Count):
            obj = paragraph.ChildObjects.get_Item(k)

            if isinstance(obj, TextRange):
                txtRange = obj if isinstance(obj, TextRange) else None
                fontName = txtRange.CharacterFormat.FontName

                if fontName == "FangSong":
                    txtRange.CharacterFormat.FontName = "LiSu"

document.SaveToFile("/replaced_font.docx", FileFormat.Docx)
document.Dispose()

How It Works

Traverse the document in the same three‑level manner.
Identify each TextRange and retrieve its current font name.
Conditional replacement – if the font name equals "FangSong", assign "LiSu" to FontName.
Save the modified document under a new name.

Extensions and Precautions

Partial replacement – if you only want to replace fonts in certain paragraphs or styles, add extra filters (e.g., by paragraph style or section) inside the loop.
Font availability – the target font (“LiSu”) must be installed on the system or embedded in the document; otherwise, Word will substitute a fallback. For production use, prefer common fonts like “SimSun” or “Times New Roman”, or embed the fonts beforehand.
Format retention – replacing the font does not affect other formatting such as bold, italic, or colour, because the CharacterFormat properties are independent.

4. Comparison and Combined Workflow

Aspect	Retrieving Fonts	Replacing Fonts
Primary goal	Auditing and analysis	Standardisation and conversion
Data flow	Document → external text file	Document → new document
Implementation complexity	Requires custom deduplication logic	Simple conditional update
Performance overhead	List membership checks (can be optimised with a set)	Negligible

These two scripts complement each other. You can run the retrieval first to get an overview of current font usage, decide which fonts to replace, and then apply the replacement. For finer control—say, replacing only a specific font at a certain size—you can merge the logic and check both FontName and FontSize during replacement.

5. Optimisation and Best Practices

Use a set for deduplication – if you don’t need colour, you can store (fontName, fontSize) tuples in a set, eliminating the custom class.
Add exception handling – wrap LoadFromFile and SaveToFile in try...except blocks to handle file permission or format issues gracefully.
Batch processing – encapsulate the core logic in a function and call it for a list of file paths.
Dispose of resources – always call document.Dispose(), especially when processing many documents in a loop, to avoid memory leaks.

6. Conclusion

With Spire.Doc for Python, we have built two essential font‑management tools in under a hundred lines of code. The library’s clear object model and straightforward API make it an excellent fit for automated workflows. Whether you are checking font compliance in financial reports or updating templates across a large corpus, this approach significantly reduces manual effort and improves accuracy. Readers are encouraged to extend the code to handle additional scenarios—such as table cells, headers, footers, or specific paragraph styles—to build even more powerful document governance solutions. Ultimately, the value of these techniques lies in solving real‑world problems, and the combination of Python and Spire.Doc delivers that promise effectively.

C# Generating Word Documents from Templates: Replacing Text and Image Placeholders

jelizaveta — Thu, 09 Jul 2026 09:58:13 +0000

In office automation development, one of the most common requirements is to batch-generate Word documents (such as contracts, reports, resumes, notices, etc.) from fixed templates. This article demonstrates how to use the Spire.Doc for .NET component to load a template file, replace text placeholders with real data, support replacing placeholders with images, and ultimately produce a complete Word document.

Why Choose Spire.Doc?

Spire.Doc is a powerful .NET Word component that allows you to create, read, edit, and convert Word documents without installing Microsoft Office. It provides a rich set of APIs – especially the Document.Replace() method for fast text substitution, and in combination with FindString() and DocPicture, it flexibly enables image insertion, making it ideal for template‑filling scenarios.

Implementation Steps

The following complete console application example demonstrates how to replace placeholders like #name# and #gender# with actual content, and replace #photo# with an image.

1. Import Namespaces and Initialize the Document

using Spire.Doc;
using Spire.Doc.Documents;
using Spire.Doc.Fields;
using System.Drawing;

Create a Document object and load the template file (.docx):

Document document = new Document();
document.LoadFromFile("Template.docx");

2. Text Placeholder Replacement

Build a dictionary where keys are placeholders (e.g., #name#) and values are the actual data. Here we localize the sample content to an English context:

Dictionary<string, string> replaceDict = new Dictionary<string, string>
{
    { "#name#", "Zhang San" },
    { "#gender#", "Male" },
    { "#birthdate#", "January 15, 1990" },
    { "#address#", "Pudong New Area, Shanghai" },
    { "#city#", "Shanghai" },
    { "#state#", "Municipality" },
    { "#postal#", "200120" },
    { "#country#", "China" }
};

Iterate through the dictionary and call document.Replace(placeholder, replacement, true, true) to perform a full‑document replacement. The two Boolean parameters specify whether to match case and whole words, respectively; set them as needed.

3. Image Placeholder Replacement

Text replacement cannot handle images, so we need a separate method ReplaceTextWithImage. Its core logic is:

Load the image with Image.FromFile();
Create a DocPicture object and load the image into it;
Locate the #photo# placeholder using document.FindString(), obtaining the TextRange;
Retrieve the index of that TextRange within its paragraph’s child objects, insert the picture at the same position, and then remove the placeholder text.

static void ReplaceTextWithImage(Document document, string stringToReplace, string imagePath)
{
    Image image = Image.FromFile(imagePath);
    DocPicture pic = new DocPicture(document);
    pic.LoadImage(image);

    TextSelection selection = document.FindString(stringToReplace, false, true);
    TextRange range = selection.GetAsOneRange();
    int index = range.OwnerParagraph.ChildObjects.IndexOf(range);

    range.OwnerParagraph.ChildObjects.Insert(index, pic);
    range.OwnerParagraph.ChildObjects.Remove(range);
}

Call it with the image path:

ReplaceTextWithImage(document, "#photo#", "portrait.png");

4. Save and Release

Finally, save the document as a new file and release resources:

document.SaveToFile("ReplacePlaceholders.docx", FileFormat.Docx);
document.Dispose();

Complete Code

using Spire.Doc;
using Spire.Doc.Documents;
using Spire.Doc.Fields;
using System.Drawing;

namespace CreateWordByReplacingTextPlaceholders
{
    class Program
    {
        static void Main(string[] args)
        {
            Document document = new Document();
            document.LoadFromFile("Template.docx");

            Dictionary<string, string> replaceDict = new Dictionary<string, string>
            {
                { "#name#", "Zhang San" },
                { "#gender#", "Male" },
                { "#birthdate#", "January 15, 1990" },
                { "#address#", "Pudong New Area, Shanghai" },
                { "#city#", "Shanghai" },
                { "#state#", "Municipality" },
                { "#postal#", "200120" },
                { "#country#", "China" }
            };

            foreach (var kvp in replaceDict)
            {
                document.Replace(kvp.Key, kvp.Value, true, true);
            }

            ReplaceTextWithImage(document, "#photo#", "portrait.png");

            document.SaveToFile("ReplacePlaceholders.docx", FileFormat.Docx);
            document.Dispose();
        }

        static void ReplaceTextWithImage(Document document, string stringToReplace, string imagePath)
        {
            Image image = Image.FromFile(imagePath);
            DocPicture pic = new DocPicture(document);
            pic.LoadImage(image);

            TextSelection selection = document.FindString(stringToReplace, false, true);
            TextRange range = selection.GetAsOneRange();
            int index = range.OwnerParagraph.ChildObjects.IndexOf(range);

            range.OwnerParagraph.ChildObjects.Insert(index, pic);
            range.OwnerParagraph.ChildObjects.Remove(range);
        }
    }
}

Notes and Extensions

Template Design : In the Word template, placeholders must exactly match the strings in the code (including case and special symbols). It is recommended to use obvious markers like # or {{}} to avoid unintended replacements.
Image Handling : The ReplaceTextWithImage method assumes that the placeholder exists independently within a paragraph. If the placeholder shares a line with other text, inserting an image may affect layout; you can adjust the insertion position according to your business logic.
Performance Optimization : For bulk document generation, you may reuse the Document object or work with memory streams.
Fonts and Styles : After text replacement, the new content inherits the style of the original placeholder. If custom styling is needed, you can manipulate the CharacterFormat property of the TextRange.

Summary

With the concise APIs provided by Spire.Doc, we have implemented data population for Word templates in just a few dozen lines of code, supporting both text and images. This approach greatly improves the efficiency and accuracy of document generation, making it well‑suited for enterprise‑level reporting, certificate printing, bulk correspondence, and more. Developers only need to focus on template design and data sources; the rest is handled by the code.

If you are looking for a lightweight, Office‑independent solution for Word manipulation, Spire.Doc is certainly worth trying. We hope this article helps you get started quickly and adds automated document generation capabilities to your projects.

Converting PDF to Word in React: A WASM-Based Approach

jelizaveta — Tue, 07 Jul 2026 02:49:39 +0000

Whether in office automation systems, online education platforms, or enterprise internal management tools, we often encounter the need to convert PDF files into editable Word documents so that users can make further modifications or extract content. Although there are many conversion tools available on the market, how to implement this functionality independently in a web frontend (especially in a React project) without relying on external API services remains a challenge.

This article will take you deep into Spire.PDF for JavaScript — a powerful frontend PDF processing library. Through its provided PdfToDocConverter class, we can directly complete PDF-to-Word conversion in the browser, without ever uploading files to the server. This not only protects data privacy but also improves response speed. Based on the provided React component code, this article breaks down the implementation process in detail and shares key pitfalls to avoid as well as optimization ideas.

1. Why Choose Spire.PDF for JavaScript?

Spire.PDF for JavaScript is a frontend PDF development component from E-iceblue. It is built on WebAssembly (WASM) technology, porting a mature .NET PDF processing engine to the browser environment. Its core advantages include:

Pure frontend processing : All computation is completed in the user's browser, requiring no backend support and reducing server load.
Rich feature set : Supports PDF creation, editing, conversion (to Word/HTML/images), merging/splitting, form filling, and more.
High‑fidelity output : Converted Word documents preserve the original PDF's layout, fonts, tables, and graphics to the greatest extent possible.
Cross‑framework compatibility : Not only suitable for React, but also integrable into Vue, Angular, or vanilla JavaScript.

2. Environment Setup and Resource Deployment

2.1 Install Dependencies

Run the following command in the root directory of your React project:

npm install spire.office

After installation, the node_modules/spire.office directory contains precompiled JS files and the WASM binary. Since the WASM file needs to be loaded from the server, we usually copy the relevant files to the public directory so they can be accessed via static paths.

2.2 Prepare Fonts and Test Files

Font rendering is crucial when converting PDF to Word. By default, Spire.PDF requires TrueType fonts (e.g., arial.ttf) to ensure correct character mapping. In addition, you will need a sample PDF file to convert (e.g., ToDocx.pdf). Place these two files under public/static/font/ and public/static/data/, respectively.

2.3 Understand the WASM Loading Process

The core of Spire.PDF is the WebAssembly module, which must be loaded and initialized asynchronously. In React, we use useEffect to accomplish this when the component mounts. In the code, we dynamically import() the spire.pdf.js file (copied from the npm package to the public folder), then call the exported factory function, passing the locateFile option to specify the path to the .wasm file.

const spireModule = await import(`${publicUrl}/spire.pdf.js`);
const rawModule = spireModule.default || spireModule;
window.wasmModule = typeof rawModule === 'function' 
  ? await rawModule({ locateFile: p => p.endsWith('.wasm') ? `${publicUrl}/${p}` : p })
  : rawModule;

Note the webpackIgnore: true comment, which tells Webpack not to process this dynamic import and to preserve the original path, avoiding resource resolution errors during bundling.

3. Line‑by‑Line Explanation of the Core Conversion Logic

3.1 Obtain the WASM Module Instance

At the beginning of the conversion function ConvertPdfToWord, we check whether window.wasmModule.spirepdf exists. This object contains all the classes and methods for PDF operations and serves as the entry point for subsequent API calls.

3.2 Load Font and PDF Files into the Virtual File System (VFS)

Spire.PDF simulates a file system (VFS) in the browser; all files to be processed must first be written into this VFS. The code calls window.spire.FetchFileToVFS, which downloads a file from a given URL and stores it at the specified path. The first parameter is the file name, the second is the target directory (empty string means the root), and the third is the URL prefix where the source file resides.

await window.spire.FetchFileToVFS("arial.ttf","/Library/Fonts/",`${process.env.PUBLIC_URL}static/font/`);
await window.spire.FetchFileToVFS("ToDocx.pdf", "", `${process.env.PUBLIC_URL}static/data/`);

Here, placing the font under /Library/Fonts/ simulates the font directory on macOS/Linux; Spire.PDF internally looks for fonts there. You can also use a custom path, but you must ensure it is correctly referenced during conversion.

3.3 Create an Instance of PdfToDocConverter

PdfToDocConverter is the dedicated converter class for PDF‑to‑Word conversion. Its constructor accepts a configuration object where filePath specifies the path to the PDF file in the VFS (i.e., the ToDocx.pdf we just loaded).

let converter = new wasmModule.PdfToDocConverter({filePath: "ToDocx.pdf"});

3.4 Set Word Document Properties

The generated Word file supports metadata such as title and author, which can be set via the DocxOptions object:

converter.DocxOptions.Subject = "Convert PDF to Word";
converter.DocxOptions.Authors = "E-ICEBLUE";

These properties will be written into the final docx file, making it easier for users to manage the document.

3.5 Perform the Conversion and Save

Call the SaveToDocx method, passing the output file name:

converter.SaveToDocx({fileName: "ToWord.docx"});

At this point, the converted file has been written to the root directory of the VFS.

3.6 Read from the VFS and Download the File

To provide the file to the user, we need to read the binary data from the VFS, construct a Blob, and create a download link. The code reads the file content via window.dotnetRuntime.Module.FS.readFile (note that dotnetRuntime is the .NET runtime instance used by Spire.PDF under the hood), then generates a Blob and triggers a click event on an a tag to start the automatic download.

const modifiedFileArray = window.dotnetRuntime.Module.FS.readFile("ToWord.docx");
const modifiedFile = new Blob([modifiedFileArray], { type: "msword" });
const url = URL.createObjectURL(modifiedFile);
const a = document.createElement('a');
a.href = url;
a.download = "ToWord.docx";
a.click();
URL.revokeObjectURL(url);

4. Complete React Component Structure and Final Code

The component's core lifecycle is very clear:

Mount phase (useEffect) : Asynchronously load the Spire.PDF WASM module and attach it to the window object for later use.
Interaction phase (ConvertPdfToWord) : Trigger the conversion process on button click — load fonts and the source file → instantiate the converter → set metadata → perform conversion → read the result from the virtual file system and trigger the browser download.
Render phase : Display the title and action button, waiting for user interaction.

For your convenience, here is the complete component code that integrates loading status indicators and error handling :

import React, { useState, useEffect } from 'react';

function App() {
  const [wasmModule, setWasmModule] = useState(null);
  const [isLoading, setIsLoading] = useState(false); // new loading state

  // 1. Initialize the WASM module
  useEffect(() => {
    (async () => {
      try {
        const publicUrl = process.env.PUBLIC_URL || '';
        const spireModule = await import(/* webpackIgnore: true */ `${publicUrl}/spire.pdf.js`);
        const rawModule = spireModule.default || spireModule;
        window.wasmModule = typeof rawModule === 'function' 
          ? await rawModule({ locateFile: p => p.endsWith('.wasm') ? `${publicUrl}/${p}` : p })
          : rawModule;       
        setWasmModule(window.wasmModule);
        console.log('Spire.PDF loaded successfully');
      } catch (error) {
        console.error('Failed to load spire.pdf.js:', error);
        alert('PDF engine failed to load. Please refresh the page and try again.');
      }
    })();
  }, []);

  // 2. Core conversion function
  const ConvertPdfToWord = async () => {
    const wasmModule = window.wasmModule?.spirepdf;
    if (!wasmModule) {
      alert('Engine not yet loaded. Please try again later.');
      return;
    }

    setIsLoading(true);
    try {
      // Load font and PDF file into the virtual file system
      await window.spire.FetchFileToVFS("arial.ttf","/Library/Fonts/",`${process.env.PUBLIC_URL}static/font/`);
      await window.spire.FetchFileToVFS("ToDocx.pdf", "", `${process.env.PUBLIC_URL}static/data/`);

      // Create the converter
      let converter = new wasmModule.PdfToDocConverter({filePath: "ToDocx.pdf"});
      converter.DocxOptions.Subject = "Convert PDF to Word";
      converter.DocxOptions.Authors = "E-ICEBLUE";

      // Execute the conversion
      const outputFileName = "ToWord.docx";
      converter.SaveToDocx({fileName: outputFileName});

      // Read and download the result
      const fileArray = window.dotnetRuntime.Module.FS.readFile(outputFileName);
      const blob = new Blob([fileArray], { type: "application/vnd.openxmlformats-officedocument.wordprocessingml.document" });
      const url = URL.createObjectURL(blob);

      const a = document.createElement('a');
      a.href = url;
      a.download = outputFileName;
      document.body.appendChild(a);
      a.click();
      document.body.removeChild(a);
      URL.revokeObjectURL(url);

    } catch (error) {
      console.error('Conversion failed:', error);
      alert('An error occurred during document conversion. Please check the console logs.');
    } finally {
      setIsLoading(false);
    }
  };

  return (
    <div style={{ textAlign: 'center', height: '300px', paddingTop: '50px' }}>
      <h1>Convert PDF to Word in React</h1>
      <button onClick={ConvertPdfToWord} disabled={isLoading}>
        {isLoading ? 'Converting, please wait...' : 'Start Conversion'}
      </button>
    </div>
  );
}

export default App;

5. Running and Testing

After starting the React development server, the page will display the title and a "Convert" button. Ensure the following files exist in the public directory:

spire.pdf.js
spire.pdf.wasm (and possibly other dependent wasm files)
static/font/arial.ttf
static/data/ToDocx.pdf

Click the button and wait a moment (depending on the PDF size and WASM initialization time). The browser will automatically download ToWord.docx. Open it with Word or WPS to check — you will find that text, images, tables, and other elements are generally preserved, giving satisfying results.

6. Performance Optimization and Important Notes

WASM loading overhead : The first load may take a few seconds. It is recommended to add a loading indicator in the UI to improve user experience.
Missing fonts : If the PDF uses special fonts, be sure to register the corresponding fonts in the VFS; otherwise, garbled characters or misalignment may occur. You can load multiple fonts in batch using FetchFileToVFS.
Large file handling : For PDFs of dozens of MB, conversion time and memory usage will increase significantly. Consider limiting file size or compressing the PDF before conversion.
Browser compatibility : WASM requires modern browsers (Chrome, Firefox, Edge, Safari) and does not support IE.
Licensing : Spire.PDF for JavaScript offers a free community edition, but it has a per‑file page limit (usually within 10 pages). For more complex documents, a commercial license is required.

7. Extended Application Scenarios

After mastering the basic flow of PDF‑to‑Word conversion, you can further explore other capabilities of Spire.PDF:

PDF to images : Render each page as PNG/JPEG for previews or thumbnail generation.
PDF merging and splitting : Concatenate multiple PDFs or extract specific pages.
Form data extraction : Automatically read the values of PDF form fields for data collection.

Combined with the React ecosystem, you can encapsulate this into a standalone DocumentConverter component that supports drag‑and‑drop upload, progress bars, batch conversion, and other advanced features, providing a desktop‑like experience for users.

8. Conclusion

Through the explanation in this article, we have successfully implemented PDF‑to‑Word conversion in a React project using Spire.PDF for JavaScript. The entire process is completely offline, secure, and efficient. The library's ease of use and powerful features make it a great asset for frontend document processing, especially suitable for B‑side applications with strict data privacy requirements.

I hope this article helps you get started quickly and apply this solution flexibly in real projects. If you encounter any issues during integration, feel free to consult the official documentation or community discussions. I also look forward to seeing you build even more brilliant solutions based on this foundation.

How to Convert Excel to PDF Using JavaScript (With Parameter Settings)

jelizaveta — Thu, 02 Jul 2026 01:23:50 +0000

Have you ever needed to turn an Excel spreadsheet into a PDF directly in the browser? It’s a very common need, whether you’re generating reports, storing data, or showing previews. PDF wins because it works everywhere and looks consistent. Thanks to Spire.XLS for JavaScript, we can do this entirely on the front end – no server, no Office installation – with just a few lines of code. This tutorial starts from zero, sets up a React example, and covers many customization options.

1. Why Choose Spire.XLS for JavaScript?

Spire.XLS for JavaScript is a pure front‑end Excel manipulation library based on WebAssembly. It allows you to create, read, modify, and convert Excel documents in the browser without installing Office or any local software. Its core advantages include:

Cross‑platform : Supports all modern browsers (Chrome, Firefox, Edge, etc.) as well as Node.js.
High performance : Built on WASM, it handles large files smoothly.
Rich features : Not only supports conversion, but also enables manipulation of cells, styles, charts, pivot tables, and more.
No network required : All processing is done locally, without relying on cloud services, ensuring data privacy.

In this article, we will focus on how to use it to convert Excel → PDF and extend it with various custom settings.

2. Installation and Initialization

First, install the spire.office package in your project:

npm i spire.office

After installation, you need to place spire.xls.js and its corresponding .wasm file in your project's public directory (e.g., public/) so that they can be dynamically loaded in the browser. If your build tool is Webpack, remember to use webpackIgnore: true to prevent the resources from being incorrectly bundled.

In React, we asynchronously load the WASM module inside useEffect and mount its instance on window for global use. The core loading logic is as follows:

const publicUrl = process.env.PUBLIC_URL || '';
const spireModule = await import(/* webpackIgnore: true */ `${publicUrl}/spire.xls.js`);
const rawModule = spireModule.default || spireModule;
window.wasmModule = typeof rawModule === 'function'
  ? await rawModule({ locateFile: p => p.endsWith('.wasm') ? `${publicUrl}/${p}` : p })
  : rawModule;

Here we specify the lookup path for the .wasm file to ensure all resources are loaded correctly. After a successful load, we can access the core Excel processing API via window.wasmModule.spirexls.

3. Basic Conversion Workflow

Converting an Excel file to PDF requires only a few core steps: load fonts and files into the virtual file system (VFS), create a Workbook object, perform the conversion, and export the download. Below is a complete React component that implements the entire process from module loading to triggering the download:

import React, { useState, useEffect } from 'react';

function App() {
  const [wasmModule, setWasmModule] = useState(null);

  // Load Spire.XLS
  useEffect(() => {
    (async () => {
      try {
        const publicUrl = process.env.PUBLIC_URL || '';
        const spireModule = await import(/* webpackIgnore: true */ `${publicUrl}/spire.xls.js`);
        const rawModule = spireModule.default || spireModule;
        window.wasmModule = typeof rawModule === 'function'
          ? await rawModule({ locateFile: p => p.endsWith('.wasm') ? `${publicUrl}/${p}` : p })
          : rawModule;
        setWasmModule(window.wasmModule);
      } catch (error) {
        console.error('Failed to load spire.xls.js WASM module:', error);
      }
    })();
  }, []);

  // Excel to PDF function
  const ExcelToPDF = async () => {
    const wasmModule = window.wasmModule.spirexls;

    if (wasmModule) {
      // 1. Load fonts into the virtual file system (VFS)
      await window.spire.FetchFileToVFS('Arial.ttf', '/Library/Fonts/', `${process.env.PUBLIC_URL}/static/font/`);

      // 2. Specify the output PDF file path
      const outputFileName = 'out.pdf';

      // 3. Load the input Excel file into the virtual file system (VFS)
      const inputFileName = 'ToPDF.xlsx';
      await window.spire.FetchFileToVFS(inputFileName, '', `${process.env.PUBLIC_URL}/static/data/`);

      // 4. Create a Workbook object and load the document
      const workbook = new wasmModule.Workbook();
      workbook.LoadFromFile({ fileName: inputFileName });

      // 5. Set conversion options (fit worksheets to one page)
      workbook.ConverterSetting.SheetFitToPage = true;

      // 6. Save as PDF format
      workbook.SaveToFile({ fileName: outputFileName, fileFormat: wasmModule.FileFormat.PDF });

      // 7. Read the generated PDF and convert it to a Blob object
      const modifiedFileArray = window.dotnetRuntime.Module.FS.readFile(outputFileName);
      const modifiedFile = new Blob([modifiedFileArray], { type: 'application/pdf' });

      // 8. Create a download link and trigger the download
      const url = URL.createObjectURL(modifiedFile);
      const a = document.createElement('a');
      a.href = url;
      a.download = outputFileName;
      document.body.appendChild(a);
      a.click();
      document.body.removeChild(a);
      URL.revokeObjectURL(url);

      // 9. Release resources
      workbook.Dispose();
    }
  };

  return (
    <div style={{ textAlign: 'center', height: '300px' }}>
      <h1> Convert Excel file to PDF using JavaScript in React </h1>
      <button onClick={ExcelToPDF} disabled={!wasmModule}>
        Convert
      </button>
    </div>
  );
}

export default App;

Step‑by‑step explanation:

Load fonts and the file to be converted into the VFS Spire.XLS relies on system fonts to render text, so we need to load the required TrueType font (e.g., Arial.ttf) into the /Library/Fonts/ directory of the VFS. Similarly, the input Excel file also needs to be stored in the VFS via FetchFileToVFS.
Create a Workbook object and load the Excel file The Workbook class represents the entire Excel document. We load the source file we just stored using LoadFromFile.
Set conversion options (optional) For example, set SheetFitToPage to true so that each worksheet fits onto one page.
Save as PDF Call SaveToFile and specify the format as wasmModule.FileFormat.PDF.
Read the generated PDF and trigger the download Use FS.readFile to obtain the PDF binary data from the VFS, create a Blob object, and then generate a download link using URL.createObjectURL.
Release resources Call workbook.Dispose() to free memory.

The steps above are fully implemented in the ExcelToPDF function in the example code. After clicking the button, the user can download the converted PDF file.

4. More Control: Customising Your PDF Output

In real‑world business scenarios, we often need more than a "one‑click conversion"; we require fine‑grained control over the conversion result. Spire.XLS provides a wealth of configuration interfaces. Here are four common customisation methods.

1. Convert Specific Worksheets (Sheets)

By default, workbook.SaveToFile converts all worksheets. If you only need to output a specific page, you can use the Worksheet.SaveToPdf method instead:

// Get the first worksheet (index starts from 0)
let sheet = workbook.Worksheets.get(0);
// Save only that worksheet as PDF
sheet.SaveToPdf({ fileName: outputFileName });

This is very useful when you only need a report summary or a particular data page.

2. Fit Each Worksheet to One Page

When a worksheet contains a lot of content, direct export may result in multiple pages or clipped content. With ConverterSetting, you can enable the "fit to page" feature with one line:

workbook.ConverterSetting.SheetFitToPage = true;

This setting scales the worksheet content as a whole, ensuring that all columns and rows fit exactly onto one page – ideal for generating overview‑type PDFs.

3. Adjust Margins

PDF margins affect the final layout aesthetics. Spire.XLS allows you to set the top, bottom, left, and right margins (in inches) individually:

let sheet = workbook.Worksheets.get(0);
sheet.PageSetup.TopMargin = 0.5;
sheet.PageSetup.BottomMargin = 0.5;
sheet.PageSetup.LeftMargin = 0.3;
sheet.PageSetup.RightMargin = 0.3;

Reasonable margin adjustments prevent content from sticking to the edges and improve the reading experience.

4. Specify Page Size

Different use cases require different page sizes – for example, contracts often use A4, while posters may require A3. You can easily switch via the PaperSize property:

sheet.PageSetup.PaperSize = wasmModule.PaperSizeType.PaperA3;

Besides A3, the library also supports common paper types such as A4, A5, Letter, Legal, etc., allowing flexible choices based on your needs.

5. Complete Example and Important Notes

The React component App provided in this article already integrates all the above functionality. You only need to place the prepared font file (.ttf) and a sample Excel file (.xlsx) in the public/static/font/ and public/static/data/ directories respectively, and you can run it and try it out.

Key considerations:

Font loading : If garbled text or blank areas appear in the PDF, it is most likely due to missing fonts. Ensure that the loaded font supports the character sets used in the Excel file.
WASM loading : Because the WASM file is relatively large, the first load may take a few seconds. It is recommended to show a loading state in the UI.
File paths : The third parameter of FetchFileToVFS is the URL path of the source file on the server – make sure it is accessible.
Memory management : Always call Dispose() after processing a document to avoid memory leaks, especially in scenarios with frequent conversions.

6. Conclusion

With Spire.XLS for JavaScript, we have moved the traditionally complex, backend‑dependent Excel‑to‑PDF task entirely to the front end, reducing server load and improving user experience. From loading the module, basic conversion, to advanced controls, this article provides you with a complete practical solution. You can extend it further – for example, by adding file upload interfaces, supporting batch conversion, or integrating watermarks.

As WebAssembly technology continues to mature, front‑end document processing capabilities will become even more powerful. Spire.XLS has already paved the way for us. Now, go ahead and give it a try!

How to Add AutoFilters to Excel in C# (Date, Color, and Text Filters)

jelizaveta — Tue, 30 Jun 2026 02:19:40 +0000

In data processing and analysis, Excel's AutoFilter feature is a powerful tool for boosting efficiency—it quickly locates the desired subset from massive datasets without requiring complex formulas. If you are a .NET developer looking to programmatically add filters to Excel files, Free Spire.XLS for .NET is a lightweight, free, and feature-rich choice. This article will walk you through three common AutoFilter scenarios using C# and this component: basic range filtering, date-group filtering, and custom text matching. All code is standardized and ready for reuse.

Preparation: Installing Free Spire.XLS

First, create a Console Application in Visual Studio (supporting .NET Framework, .NET Core, or .NET 5+). Install Free Spire.XLS via the NuGet Package Manager:

Install-Package FreeSpire.XLS

Or via the .NET CLI:

dotnet add package FreeSpire.XLS

This library provides full read/write support for Excel 97–2016 formats and does not require Microsoft Office to be installed. The examples in this article use a test file named Data.xlsx; you can adjust the path as needed.

1. Basic AutoFilter: Setting the Filter Range

The simplest scenario is to enable the AutoFilter on a specified column (or row) so that users can manually choose criteria later in Excel. The following code loads a workbook, gets the first worksheet, and sets the AutoFilter range to the header row (e.g., A1:C1), thereby adding drop‑down arrows for each column in that region.

using Spire.Xls;

namespace AddAutoFilterDemo
{
    class Program
    {
        static void Main(string[] args)
        {
            // 1. Initialize Workbook and load the Excel file
            using (Workbook workbook = new Workbook())
            {
                workbook.LoadFromFile(@"C:\Data\Data.xlsx");

                // 2. Get the first worksheet
                Worksheet sheet = workbook.Worksheets[0];

                // 3. Set the AutoFilter range (typically the header row)
                sheet.AutoFilters.Range = sheet.Range["A1:C1"];

                // 4. Save the result (Excel 2016 version can be specified)
                workbook.SaveToFile("AutoFilter_Base.xlsx", ExcelVersion.Version2016);
            }
        }
    }
}

Note : The AutoFilters.Range property determines the filtering scope. Generally, you only need to select the header cells, and the filter will automatically cover all data rows in those columns. When you open the saved file, you will see filter buttons on each column header, allowing you to filter as needed.

2. Built‑in Date Filtering: Grouping by Year/Month/Day

A more common requirement is to filter date fields by specific months, quarters, or years. Free Spire.XLS provides the AddDateFilter method, which supports grouping by year, month, day, hour, minute, etc. For example, the following code filters all records for February 2022 :

using Spire.Xls;
using Spire.Xls.Core.Spreadsheet.AutoFilter;

namespace AddAutoFilterDemo
{
    class Program
    {
        static void Main(string[] args)
        {
            using (Workbook workbook = new Workbook())
            {
                workbook.LoadFromFile(@"C:\Data\Data.xlsx");

                Worksheet sheet = workbook.Worksheets[0];

                // Set the filter range to A1:A12 (assuming column A contains dates)
                sheet.AutoFilters.Range = sheet.Range["A1:A12"];

                // Get the column to filter (index 0 means the first column)
                IAutoFilter filterColumn = sheet.AutoFilters[0];

                // Add date grouping: February 2022
                sheet.AutoFilters.AddDateFilter(
                    filterColumn,
                    DateTimeGroupingType.Month,  // group by month
                    2022,                        // year
                    2,                           // month
                    0, 0, 0, 0                  // day, hour, minute, second (not used here)
                );

                // Apply the filter
                sheet.AutoFilters.Filter();

                workbook.SaveToFile("AutoFilter_Date.xlsx", ExcelVersion.Version2016);
            }
        }
    }
}

Key Point : The DateTimeGroupingType enum supports Year, Quarter, Month, Day, Hour, Minute, Second, etc., which you can combine as needed. For example, to filter the first quarter of 2022, set the type to Quarter, year to 2022, and quarter parameter to 1.

Additional Note : The example above demonstrates date grouping (AddDateFilter), but the AutoFilters object provides several other built‑in filtering methods, allowing flexible use across different business scenarios:

Filter by fill color – Use the AddFillColorFilter method to filter rows whose cells have a specific background color.
Filter by font color – Use the AddFontColorFilter method to filter by cell font color.
Filter by icon – Use the AddIconFilter method to filter rows that contain a specific icon from conditional formatting (e.g., traffic lights, arrows).

These methods work similarly to AddDateFilter: you specify the filter column, pass the color or icon parameters, and finally call Filter() to apply. You can freely combine them based on your actual data types to achieve highly customized filtering logic. This article uses date filtering only as an illustration; in practice, do not limit yourself to date grouping.

3. Custom Filtering: Matching Specific Text or Numeric Values

Sometimes we need to filter rows containing a specified string (or equal to a numeric value). In such cases, we can use the CustomFilter method. The following example filters all rows in column G where the value is "Grocery" (for exact text matching):

using Spire.Xls;
using Spire.Xls.Core.Spreadsheet.AutoFilter;

namespace AddAutoFilterDemo
{
    class Program
    {
        static void Main(string[] args)
        {
            using (Workbook workbook = new Workbook())
            {
                workbook.LoadFromFile(@"C:\Data\Data.xlsx");

                Worksheet sheet = workbook.Worksheets[0];

                // Set the filter range to G1:G12
                sheet.AutoFilters.Range = sheet.Range["G1:G12"];

                // Get the column object (explicitly cast to FilterColumn)
                FilterColumn filterColumn = (FilterColumn)sheet.AutoFilters[0];

                // Add a custom filter condition: equal to "Grocery"
                sheet.AutoFilters.CustomFilter(
                    filterColumn,
                    FilterOperatorType.Equal,    // equal operator
                    "Grocery"                    // comparison value
                );

                // Apply the filter
                sheet.AutoFilters.Filter();

                workbook.SaveToFile("AutoFilter_Custom.xlsx", ExcelVersion.Version2016);
            }
        }
    }
}

Besides Equal, FilterOperatorType also offers LessThan, GreaterThan, Contains, BeginsWith, EndsWith, and many other options, covering most fuzzy‑matching needs. To combine multiple conditions (e.g., "contains A or B"), you can call CustomFilter multiple times and combine them with logical operators.

Advanced Tips and Precautions

Path Handling : The examples use absolute paths; in real projects, it is recommended to use relative paths or read from configuration, e.g., Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "Data.xlsx").
Performance Optimization : For large Excel files, consider using Workbook.LoadFromFile(fileName, ExcelOpenType.Automatic) to optimize memory usage during loading.
Working with Filtered Data : After applying the filter, you can iterate over sheet.Rows and check the IsHidden property to further process visible rows.
Multi‑column Filtering : To filter multiple columns simultaneously, simply add conditions for each column in order, then call Filter() once at the end.
Clearing Filters : To remove all filters, call sheet.AutoFilters.Clear() and save again.

Summary

With Free Spire.XLS for .NET, just a few lines of C# code can empower Excel with robust AutoFilter capabilities. Whether enabling basic column filters, using built‑in date grouping, or applying custom text matching, the component offers intuitive APIs and stable performance. The three examples in this article cover the most common filtering scenarios, which you can flexibly combine based on your actual data characteristics. Finally, don’t forget to share the generated Excel files with colleagues or downstream systems to make data insights more efficient.

If you wish to explore further, you can also investigate other methods under AutoFilters, such as AddDynamicFilter (for top N dynamic filtering) or AddColorFilter (for filtering by cell color). Mastering these techniques will make your Excel automation toolbox even more complete.

Converting HTML to Word or Word to HTML Using C#

jelizaveta — Thu, 25 Jun 2026 06:11:53 +0000

As the standard format for web content, HTML and the common office document format Word often need to be converted between each other in many enterprise-level applications. Whether it is generating Word reports from dynamic content in a database or publishing existing Word documents as web pages, mastering efficient conversion methods can significantly improve productivity.

This article introduces how to use Spire.Doc for .NET, a professional document processing component, to easily convert between HTML and Word using C# code.

Why Choose Spire.Doc for .NET?

The .NET framework does not natively support manipulating or converting Word or HTML documents. While there are open-source alternatives (such as HtmlAgilityPack combined with OpenXML SDK), they often require developers to handle many low-level details manually and may fall short in style preservation, image embedding, and other areas.

Spire.Doc for .NET provides a powerful and easy-to-use API that enables document creation, editing, and conversion without installing Microsoft Office on the server. It excels in conversion quality:

Style Preservation : Full support for CSS styles in HTML, including text formatting, colors, alignment, and more.
Image Handling : Automatically recognizes and embeds <img> tags from HTML.
Table Structure : Preserves the original layout of HTML tables to avoid misalignment.
Development Complexity : The API is cleanly designed with a low learning curve.

Preparation: Installing Spire.Doc

Before coding, you need to add a reference to Spire.Doc in your project. The recommended approach is to run the following command in the NuGet Package Manager Console:

Install-Package Spire.Doc

Scenario 1: Converting an HTML String to Word

This scenario is highly flexible and suitable for fetching HTML content from databases, API endpoints, or other dynamic data sources, and generating Word documents on the fly.

The following code demonstrates how to read the content of an HTML file (as a string) and convert it to a .docx file:

using Spire.Doc;
using Spire.Doc.Documents;
using System.IO;

namespace ConvertHtmlStringToWord
{
    class Program
    {
        static void Main(string[] args)
        {
            // 1. Create a Document object
            Document document = new Document();

            // 2. Add a section
            Section section = document.AddSection();
            // Optional: set page margins
            section.PageSetup.Margins.All = 2;

            // 3. Add a paragraph
            Paragraph paragraph = section.AddParagraph();

            // 4. Read the HTML string from a file
            string htmlFilePath = @"C:\Users\Administrator\Desktop\Html.html";
            string htmlString = File.ReadAllText(htmlFilePath, System.Text.Encoding.UTF8);

            // 5. Append the HTML string to the paragraph
            paragraph.AppendHTML(htmlString);

            // 6. Save as a Word document
            document.SaveToFile("AddHtmlStringToWord.docx", FileFormat.Docx);

            // 7. Release resources
            document.Dispose();
        }
    }
}

Key Method Explanation :

Paragraph.AppendHTML(htmlString) is the core of the conversion. It parses the incoming HTML string and fully converts its formatting, images, and layout into Word paragraph content.
document.SaveToFile() allows you to specify the output format as FileFormat.Docx.

Scenario 2: Converting an HTML File Directly to Word

If you already have an existing HTML file and want to convert it directly to a Word document, the code is even more concise:

using Spire.Doc;

namespace ConvertHtmlToWord
{
    class Program
    {
        static void Main(string[] args)
        {
            // 1. Create a Document object
            Document document = new Document();

            // 2. Load the HTML file directly
            document.LoadFromFile(@"C:\Users\Administrator\Desktop\MyHtml.html", FileFormat.Html);

            // 3. Save as a Word document
            document.SaveToFile("HtmlToWord.docx", FileFormat.Docx);

            // 4. Release resources
            document.Dispose();
        }
    }
}

This approach does everything in one step. The Document.LoadFromFile() method directly supports loading files in FileFormat.Html format, eliminating the need to manually read the file content.

Key Considerations

In practice, keep the following points in mind to ensure the best conversion results:

CSS Style Support : Spire.Doc supports most common CSS styles, such as font-size, color, text-align, and more. However, for very complex CSS layouts, there may be minor differences in the output.
Image Paths : <img> tags in HTML are automatically converted to images in Word. Make sure the image paths (whether local paths or network URLs) are accessible during conversion.
Table Layout : For tables with complex structures, it is advisable to avoid special layout attributes like table-layout: fixed to ensure the converted tables display correctly in Word.
Performance : For large files with substantial content or high-resolution images, it is recommended to perform the conversion in a background thread or asynchronous task to avoid blocking the main UI thread.

Summary

With Spire.Doc for .NET, developers can achieve high-quality, high-fidelity conversions between HTML and Word in C# projects with minimal code. Whether you are dealing with dynamically generated HTML strings or batch-converting existing HTML files, this library provides a stable and efficient solution. It helps you quickly build automated document processing workflows and focus more on your business logic.

Modifying or Replacing Fonts in Word Using Python

jelizaveta — Wed, 24 Jun 2026 01:35:18 +0000

In daily office work, adjusting fonts in Word documents is a frequent need—whether it's unifying report styles, highlighting key information, or batch-processing large numbers of documents, manual operations are often inefficient. This article introduces how to use the Spire.Doc for Python library to efficiently modify font styles in Word documents programmatically.

1. Environment Setup

First, install Spire.Doc for Python via pip:

pip install Spire.Doc

After installation, import the required modules in your Python script:

from spire.doc import *
from spire.doc.common import *

2. Method 1: Modifying the Font of an Entire Paragraph

When you need to uniformly adjust the font of all text in a specific paragraph, you can achieve this by creating a paragraph style and applying it to the target paragraph.

Core steps : Load document → Get target paragraph → Create paragraph style → Set font properties → Apply style → Save document.

from spire.doc import *
from spire.doc.common import *

# Create a Document instance
document = Document()

# Load a Word document
document.LoadFromFile('input.docx')

# Get the first section
section = document.Sections[0]

# Get a specific paragraph (index starts from 0)
paragraph = section.Paragraphs[2]

# Create a paragraph style
style = ParagraphStyle(document)
style.Name = 'NewStyle'
style.CharacterFormat.Bold = True          # Bold
style.CharacterFormat.Italic = True        # Italic
style.CharacterFormat.TextColor = Color.get_Red()   # Red color
style.CharacterFormat.FontName = 'Cambria' # Font name
document.Styles.Add(style)

# Apply the style to the paragraph
paragraph.ApplyStyle(style.Name)

# Save the result document
document.SaveToFile('ChangeFontOfParagraph.docx', FileFormat.Docx)

Code explanation :

The ParagraphStyle class is used to create custom paragraph styles, where the CharacterFormat property controls font name, size, color, bold, italic, and other formatting.
Styles.Add() adds the style to the document's style collection.
The ApplyStyle() method applies the style to the specified paragraph.

This approach is suitable for uniform formatting of entire paragraphs , such as unifying the font style of headings or body text paragraphs.

3. Method 2: Finding Specific Text and Modifying Its Font

When you only need to change the font of certain keywords or phrases in the document, you can use the text search feature to precisely locate them.

Core steps : Load document → Search for target text → Iterate through matches → Modify font properties → Save document.

from spire.doc import *
from spire.doc.common import *

# Create a Document instance
document = Document()

# Load a Word document
document.LoadFromFile('input.docx')

# Search for specified text (third parameter True means case-sensitive)
textSelections = document.FindAllString('programming language', False, True)

# Modify the font style of the found text
for selection in textSelections:
    selection.GetAsOneRange().CharacterFormat.TextColor = Color.get_Red()
    selection.GetAsOneRange().CharacterFormat.Bold = True

# Save the result document
document.SaveToFile('ChangeFontOfText.docx', FileFormat.Docx)

Code explanation :

The FindAllString() method finds all matching text in the document and returns a list of TextSelection objects.
GetAsOneRange() converts the found text into a TextRange object.
Through the CharacterFormat property, you can finely control font color, bold, font size, etc.

This approach is suitable for keyword highlighting or specific term formatting , as it is precise and does not affect other text.

4. Comparison of the Two Methods

Comparison Dimension	Method 1 (Paragraph Style)	Method 2 (Text Search)
Scope	Entire paragraph	Specific text fragments
Use case	Uniform paragraph formatting	Keyword highlighting, term annotation
Flexibility	Medium	High
Preserves original styles?	Replaces entire paragraph style	Modifies only font properties, preserves other formatting

5. Summary

With Spire.Doc for Python, we can easily achieve programmatic control over font styles in Word documents. Whether it's uniformly formatting entire paragraphs via paragraph styles, or precisely locating and modifying specific words through text search, both approaches can greatly improve document processing efficiency. Once you master these two methods, you can flexibly choose the appropriate one based on your actual needs, leaving repetitive font adjustment tasks to be automatically handled by code.

Convert PDF to JPG in C#: High-Quality Rendering with Spire.PDF

jelizaveta — Thu, 18 Jun 2026 01:26:17 +0000

In document management systems, electronic archiving platforms, and online document preview applications, converting PDF files to images is a fundamental and essential capability. Whether you need to generate document thumbnails, display content across different platforms, or digitally archive historical records, PDF-to-image conversion plays a critical role. For .NET developers, Spire.PDF for .NET provides a powerful and efficient solution that enables high-fidelity PDF-to-image conversion without relying on third-party software such as Adobe Acrobat.

This article starts with the basics and gradually explores more advanced scenarios, including DPI control and stream-based processing, demonstrating how to convert PDF files to JPG images using C# and Spire.PDF.

Environment Setup

Before writing any code, install Spire.PDF into your project through NuGet. In Visual Studio, navigate to Tools > NuGet Package Manager > Manage NuGet Packages for Solution , search for Spire.PDF , and install it. Alternatively, run the following command in the Package Manager Console:

PM> Install-Package Spire.PDF

It is important to note that the free edition of Spire.PDF includes certain limitations, such as converting only a limited number of pages. For full functionality in production environments, a commercial license is required.

The Core Method: SaveAsImage

The SaveAsImage() method provided by the PdfDocument class is the foundation of PDF-to-image conversion. It offers several overloads to accommodate different requirements:

SaveAsImage(int pageIndex, PdfImageType imageType) Converts a specified page to an image, where pageIndex is a zero-based page index and imageType defines the output image format (typically PdfImageType.Bitmap).
SaveAsImage(int pageIndex, PdfImageType imageType, int dpiX, int dpiY) Converts a page while specifying horizontal and vertical DPI values, allowing you to control image quality and file size.

Basic Conversion: Export a Single Page

The following example demonstrates the simplest way to convert the first page of a PDF document to a JPG image:

using Spire.Pdf.Graphics;
using Spire.Pdf;
using System.Drawing.Imaging;
using System.Drawing;

namespace ConvertSpecificPageToPng
{
    class Program
    {
        static void Main(string[] args)
        {
            PdfDocument doc = new PdfDocument();
            doc.LoadFromFile("C:\\Users\\Administrator\\Desktop\\sample.pdf");
            Image image = doc.SaveAsImage(0, PdfImageType.Bitmap);
            image.Save("ToJPG.jpg", ImageFormat.Jpeg);
            doc.Dispose();
        }
    }
}

The conversion workflow consists of four straightforward steps:

Create a PdfDocument instance.
Load the target PDF file.
Call SaveAsImage() to render the specified page into a System.Drawing.Image object.
Save the image as a JPG file using the Image.Save() method.

Batch Conversion: Processing Multi-Page PDFs

In real-world applications, you often need to convert all pages or a subset of pages within a PDF document. By iterating through the doc.Pages collection and calling SaveAsImage() for each page, you can convert an entire document:

for (int i = 0; i < doc.Pages.Count; i++)
{
    Image image = doc.SaveAsImage(i, PdfImageType.Bitmap);
    string fileName = string.Format("Output\\ToJPG-{0}.jpg", i);
    image.Save(fileName, ImageFormat.Jpeg);
}

If only a specific page range is required—for example, the first three pages—you can limit the loop accordingly:

for (int i = 1; i <= 3; i++)
{
    Image image = doc.SaveAsImage(i, PdfImageType.Bitmap);
    string fileName = string.Format("Output\\ToJPG-{0}.jpg", i);
    image.Save(fileName, ImageFormat.Jpeg);
}

DPI Control: Ensuring High-Quality Output

DPI (Dots Per Inch) is one of the most important factors affecting image clarity. Higher DPI values produce images with more pixels and finer details, but they also increase file size. Spire.PDF allows developers to precisely control output resolution by specifying dpiX and dpiY when calling SaveAsImage():

Image image = doc.SaveAsImage(0, PdfImageType.Bitmap, 300, 300);
image.Save("ToJPG.jpg", ImageFormat.Jpeg);

Different use cases require different DPI settings:

96–150 DPI : Suitable for web previews and online viewing.
300 DPI : Recommended for printing and professional document output.
600 DPI or higher : Ideal for high-resolution archiving and preserving fine document details.

Choosing the right DPI involves balancing image quality against storage and bandwidth requirements.

Stream-Based Processing: Saving to Memory

In some scenarios—such as uploading images directly to cloud storage or returning image data through a Web API—saving the converted image to disk is not the most efficient approach. Instead, you can write the image directly to a MemoryStream and obtain the resulting byte array:

using (MemoryStream ms = new MemoryStream())
{
    pdf.SaveAsImage(0, PdfImageType.Bitmap, 300, 300).Save(ms, ImageFormat.Jpeg);
    byte[] imageBytes = ms.ToArray();
}

This approach eliminates the need to create and clean up temporary files, resulting in cleaner code and improved performance. It is especially useful in server-side applications and cloud-based workflows.

Conclusion

With the SaveAsImage() method, Spire.PDF for .NET provides a comprehensive and flexible solution for converting PDF documents to images. From basic single-page exports to batch conversion of multi-page PDFs, from DPI customization to memory-stream output, the library supports a wide range of business requirements.

By combining these techniques according to your application's needs, you can build efficient, reliable, and high-quality PDF-to-JPG conversion workflows while maintaining an optimal balance between image fidelity and system performance.

How to Convert Excel Data to Lists, Dictionaries, and Objects in Python

jelizaveta — Tue, 16 Jun 2026 03:20:37 +0000

In Python backend development, data analytics, and office automation workflows, reading Excel files and converting them into efficient, program-friendly data structures is a common requirement.

Many developers start by accessing spreadsheet data using numeric indexes such as row[2] or col[5]. While this approach is quick to implement, it introduces a serious row/column coupling problem . The code becomes difficult to read and maintain, and even a minor change to the spreadsheet layout—such as reordering, inserting, or removing columns—can break large portions of the application.

In this article, we'll use Spire.XLS for Python to demonstrate a three-level evolution of Excel data modeling:

Raw two-dimensional lists
Lists of dictionaries
Lists of custom business objects

Each approach serves different use cases and complexity levels. By moving beyond hardcoded indexes, you can make your Excel-processing code more readable, maintainable, and scalable.

Prerequisites

All examples in this article use the spire.xls package, which provides Excel reading, writing, formatting, and batch-processing capabilities without requiring Microsoft Excel to be installed.

Install the library with:

pip install spire.xls

Approach 1: Store Excel Data as a Two-Dimensional List

How It Works

This is the most straightforward way to read Excel data. We iterate through the worksheet's used range row by row and cell by cell, storing everything in a two-dimensional list that preserves the original spreadsheet structure.

Complete Example

from spire.xls import Workbook

# Load the workbook and worksheet
workbook = Workbook()
workbook.LoadFromFile("SalesReport.xlsx")
sheet = workbook.Worksheets[0]

# Get the used range
cell_range = sheet.AllocatedRange

# Store all data in a 2D list
excel_data = []

for row_idx in range(cell_range.RowCount):
    single_row = []

    for col_idx in range(cell_range.ColumnCount):
        # Spire.XLS uses 1-based indexes
        single_row.append(
            cell_range[row_idx + 1, col_idx + 1].Value
        )

    excel_data.append(single_row)

# Release resources
workbook.Dispose()

Pros and Cons

Advantages

Extremely simple implementation
Preserves the original row-and-column structure
No additional transformation overhead

Drawbacks

All data access relies on numeric indexes:

excel_data[row][col]

The code contains no semantic meaning. If the spreadsheet structure changes, index references throughout the application must be updated manually, making maintenance difficult and error-prone.

Best For

Quick prototypes
One-off scripts
Matrix calculations
Temporary data inspection

For production applications, this structure is rarely ideal.

Approach 2: Convert Rows into Dictionaries

How It Works

To eliminate hardcoded column indexes, we can use the first row as column headers and map each subsequent row into a dictionary.

Instead of retrieving data by position, we access it by field name. This removes the dependency on column order and greatly improves readability.

Complete Example

from spire.xls import Workbook

workbook = Workbook()
workbook.LoadFromFile("SalesReport.xlsx")
sheet = workbook.Worksheets[0]

cell_range = sheet.AllocatedRange

# Extract headers from the first row
rows = list(cell_range.Rows)

headers = [
    cell_range[1, col_idx + 1].Value
    for col_idx in range(cell_range.ColumnCount)
]

# Build a list of dictionaries
data_list = []

for row in rows[1:]:  # Skip header row
    row_dict = {}

    for idx, cell in enumerate(row.Cells):
        row_dict[headers[idx]] = cell.Value

    data_list.append(row_dict)

workbook.Dispose()

Pros and Cons

Advantages

Data can now be accessed using meaningful field names:

data_list[0]["Sales"]

Benefits include:

Improved readability
Independence from column order
Easy JSON serialization
Convenient integration with APIs and data-processing pipelines
Better compatibility with Pandas workflows

Drawbacks

Dictionary-based structures are still weakly typed. Values may require additional validation and type conversion before use.

Best For

Data import/export workflows
Data cleaning tasks
API payload generation
Business reporting
General-purpose Excel processing

For most applications, this is often the best balance between simplicity and maintainability.

Approach 3: Map Rows to Custom Business Objects

How It Works

When working with fixed schemas and more complex business logic, dictionaries can become limiting. They offer no type safety, no IntelliSense support, and no natural place to encapsulate business rules.

A more robust approach is to define a business entity class and map each Excel row to an object instance.

This creates a strongly typed model that supports validation, business methods, and better developer tooling.

Complete Example

# Business entity definition
class Employee:
    def __init__(self, name: str, age: int | None, department: str):
        self.name = name
        self.age = age
        self.department = department

    def is_adult(self) -> bool:
        """Return True if the employee is an adult."""
        return self.age >= 18 if self.age else False


from spire.xls import Workbook

workbook = Workbook()
workbook.LoadFromFile("EmployeeData.xlsx")

sheet = workbook.Worksheets[0]
cell_range = sheet.AllocatedRange

employee_list = []

# Skip the header row
for row in list(cell_range.Rows)[1:]:

    name = row.Cells[0].Value

    age = (
        int(row.Cells[1].Value)
        if row.Cells[1].Value
        else None
    )

    department = row.Cells[2].Value

    employee = Employee(
        name,
        age,
        department
    )

    employee_list.append(employee)

workbook.Dispose()

Advantages and Use Cases

Benefits

Strong typing through explicit type conversion
Better data validation
IDE auto-completion and type hints
Encapsulation of business logic
Improved maintainability
Cleaner object-oriented design

For example:

employee.is_adult()

Business rules can be implemented directly within the entity instead of being scattered throughout the codebase.

Best For

Enterprise applications
Stable, well-defined data schemas
Systems with complex business rules
Long-term maintainable projects

Choosing the Right Structure

The best choice depends on the complexity of your application and how the data will be used.

Structure	Advantages	Drawbacks	Recommended Use Cases
Two-Dimensional List	Simple, fast, minimal transformation	Hardcoded indexes, poor readability, difficult maintenance	Quick scripts, data previews, matrix operations
List of Dictionaries	Readable, flexible, serialization-friendly	Weak typing, limited validation	Data analysis, data synchronization, API integration
List of Custom Objects	Strong typing, extensible, business-logic friendly	Requires additional class definitions	Enterprise projects, stable schemas, complex business workflows

Important: Always Release Workbook Resources

Every example in this article calls:

workbook.Dispose()

This is an important best practice when working with Spire.XLS .

The library maintains file handles while a workbook is open. If resources are not explicitly released, long-running applications or batch-processing jobs may encounter issues such as:

Locked Excel files
Increased memory usage
Inability to modify files later
Resource leaks in server environments

For this reason, you should always dispose of the workbook once processing is complete.

Conclusion

The progression from:

Numeric indexes in two-dimensional lists
Semantic field access with dictionaries
Strongly typed business objects

reflects a broader evolution from data-oriented programming toward business-oriented modeling .

Simple scripts do not require elaborate abstractions. For most real-world Excel-processing tasks, a list of dictionaries offers an excellent balance between flexibility and maintainability. When your application involves complex business rules and stable schemas, custom entity objects become the most robust long-term solution.

Choosing the right data structure can significantly reduce code complexity, improve readability, minimize bugs, and make your Excel-processing workflows easier to maintain as your projects grow.

How to Add a Digital Signature to a Word Document in C#

jelizaveta — Thu, 11 Jun 2026 02:48:20 +0000

When working with electronic contracts, legal documents, or official reports, adding a digital signature to a Word document is often essential. A digital signature helps verify the authenticity of a document, confirms the identity of the signer, and ensures that the content has not been modified after signing.

In this article, you'll learn how to digitally sign a Word document in C# using Free Spire.Doc for .NET . The process requires only a few lines of code and can be implemented in just a few minutes.

Prerequisites

1. Install Free Spire.Doc for .NET

You can install FreeSpire.Doc through the NuGet Package Manager or download the DLL package and add it to your project manually.

2. Prepare a Digital Certificate

You'll need a valid digital certificate in .pfx format along with its password. The certificate can be obtained from a trusted Certificate Authority (CA) or generated locally for testing purposes.

Add a Digital Signature to a Word Document

The following example demonstrates how to load a Word document, apply a digital signature using a certificate, and save the signed document.

using Spire.Doc;

namespace DigitallySignWord
{
    class Program
    {
        static void Main(string[] args)
        {
            // Create a Document object
            Document doc = new Document();

            // Load the Word document to be signed
            doc.LoadFromFile("Input.docx");

            // Specify the certificate file and password
            string certificatePath = "certificate.pfx";
            string password = "password";

            // Digitally sign the document and save it
            doc.SaveToFile("DigitallySigned.docx", FileFormat.Docx2013, certificatePath, password);
        }
    }
}

How the Code Works

Load the document The Document.LoadFromFile() method loads the target .docx file into memory.
Configure the certificate The certificatePath and password variables specify the location of the digital certificate and its associated password.
Apply the digital signature The overloaded SaveToFile() method accepts the output format, certificate path, and certificate password. When the document is saved, the digital signature is automatically embedded into the file.

After signing, open the resulting document in Microsoft Word. Word will indicate that the document contains a valid digital signature. You can view signature details by navigating to File > Info > View Signatures .

Important Notes

1. Free Version Limitations

The free edition of Spire.Doc has processing limitations. It supports Word documents containing up to 500 paragraphs or 25 tables , whichever limit is reached first.

For most small business documents, reports, and contracts, this limitation is typically sufficient.

2. Certificate Validity

For production environments, it is recommended to use a certificate issued by a trusted Certificate Authority (CA). Otherwise, Microsoft Word may display warnings indicating that the signature cannot be verified.

3. Supported Output Formats

The FileFormat parameter supports multiple Word versions, including:

Docx2010
Docx2013
Docx2016

Choose the format that best matches your compatibility requirements.

4. The Original Document Remains Unchanged

The digital signature is applied when saving a new file. The original document is not modified unless you explicitly overwrite it.

Conclusion

Free Spire.Doc for .NET provides a simple and efficient way to digitally sign Word documents in C#. With just a few lines of code, you can automate the signing process for contracts, reports, and other business documents.

When using the free edition, be sure to keep its paragraph and table limitations in mind to avoid runtime exceptions. If your documents exceed those limits, consider upgrading to a commercial license or implementing a document-splitting strategy.

By incorporating digital signatures into your workflow, you can improve document security, streamline approval processes, and ensure the integrity of your files with minimal development effort.

Extract Text and Tables from Word Documents Accurately Using Python

jelizaveta — Tue, 09 Jun 2026 02:06:42 +0000

Extracting structured content from Word documents—especially text and tables—is a common requirement in data processing workflows. While Python offers several libraries such as python-docx, handling complex document layouts or extracting both body text and tables reliably can be challenging. In these scenarios, Free Spire.Doc for Python provides a more stable and feature-rich solution.

In this article, you'll learn how to extract text from Word documents and save it to TXT files, as well as how to automatically export table data for further processing.

Prerequisites

Free Spire.Doc for Python is a powerful Word processing library that supports multiple document formats, including .doc and .docx. You can install it easily using pip:

pip install spire.doc.free

By default, the library runs in free mode. The free edition can process documents containing up to 500 paragraphs or 25 tables , which is sufficient for most small documents and testing scenarios.

Extract All Text and Save It to a TXT File

The built-in GetText() method can retrieve all text from a Word document. In practical applications, you'll often want to save the extracted content rather than simply displaying it. The following example reads all text from a Word document and writes it to a .txt file:

from spire.doc import *
from spire.doc.common import *

# Create a Document object and load a Word file
doc = Document()
doc.LoadFromFile("input.docx")

# Extract all plain text from the document
full_text = doc.GetText()

# Write the text to a TXT file
with open("output.txt", "w", encoding="utf-8") as file:
    file.write(full_text)

doc.Close()
print("Text extraction completed. Saved to output.txt")

Key Points

GetText() extracts all textual content in reading order, including paragraphs, headings, headers, and footers, while ignoring non-text elements such as images and shapes.
The file is saved using UTF-8 encoding to ensure proper handling of non-English characters.
Always call doc.Close() after processing to release system resources.

Extract and Export All Tables Accurately

Tables in Word documents often contain important business data such as reports, inventories, and records. Spire.Doc provides a clear object hierarchy:

Document → Section → Table → Row → Cell → Paragraph

The following code traverses every table in each section and exports each table as an individual .txt file. Tab characters are used as column separators, making the output easy to import into Excel or other data-processing tools.

from spire.doc import *
from spire.doc.common import *
import os

# Create the output directory
output_dir = "output/Tables"
os.makedirs(output_dir, exist_ok=True)

# Load the Word document
doc = Document()
doc.LoadFromFile("Sample.docx")

# Traverse all sections
for section_idx in range(doc.Sections.Count):
    section = doc.Sections.get_Item(section_idx)
    tables = section.Tables

    for table_idx in range(tables.Count):
        table = tables.get_Item(table_idx)
        table_data = ""

        # Traverse all rows and cells
        for row_idx in range(table.Rows.Count):
            row = table.Rows.get_Item(row_idx)

            for col_idx in range(row.Cells.Count):
                cell = row.Cells.get_Item(col_idx)

                # Collect text from all paragraphs within the cell
                cell_text = ""
                for para_idx in range(cell.Paragraphs.Count):
                    cell_text += cell.Paragraphs.get_Item(para_idx).Text + " "

                table_data += cell_text.strip()

                # Separate columns with tabs
                if col_idx < row.Cells.Count - 1:
                    table_data += "\t"

            table_data += "\n"  # End of row

        # Save the current table
        output_path = f"{output_dir}/WordTable_{section_idx+1}_{table_idx+1}.txt"

        with open(output_path, "w", encoding="utf-8") as f:
            f.write(table_data)

        print(f"Saved: {output_path}")

doc.Close()

Code Explanation

The nested loops ensure that every table in every section is processed.
Cell content is extracted by iterating through the cell's Paragraphs collection, preventing the loss of text separated by line breaks or formatting.
Output files are named using the pattern SectionIndex_TableIndex, making it easy to identify the source of each exported table.

Note: This example processes only top-level tables. If your document contains nested tables inside cells, you can extend the logic recursively to handle deeper table structures.

Best Practices and Considerations

1. Performance and Memory Usage

For large documents containing hundreds of pages, process only the content you need. If you're interested solely in tables, skip text extraction, and vice versa. Also, make sure doc.Close() is always executed to avoid resource leaks.

2. Handling Merged Cells

When tables contain merged rows or columns, the code above still extracts the text from each cell correctly. However, the exported plain-text output will not preserve merge relationships.

If maintaining the original table structure is important, you can use properties such as Cell.ColumnSpan and Cell.RowSpan to build a matrix that represents merged cells.

3. Free Edition Limitations

The free edition of Spire.Doc can process documents containing up to 500 paragraphs or 25 tables , which is generally sufficient for everyday document-processing tasks and evaluation purposes.

Conclusion

With Free Spire.Doc for Python , you can extract text and tables from Word documents using only a few dozen lines of code. The two techniques demonstrated in this article—saving document text to TXT files and exporting tables individually—can be integrated directly into your data-processing workflows.

Combined with Python's file-handling capabilities and downstream tools such as pandas, these methods make it easy to build automated document-parsing solutions.

If you need to work with more complex layouts or preserve advanced table structures, Spire.Doc also offers additional APIs such as ExportToHtml() and SaveToFile(), providing even greater flexibility for document-processing projects.

How to Convert Word to Markdown (and Back) with Python

jelizaveta — Fri, 05 Jun 2026 01:55:44 +0000

In writing, technical documentation management, and knowledge base construction, Word and Markdown are the two document formats we most frequently deal with. Word, with its powerful typesetting capabilities and mature collaborative review features, dominates enterprise office work and formal reports; while Markdown, with its plain text, lightweight nature, and ease of version control, is deeply loved by programmers and technical writers.

However, the format barrier between the two often causes headaches — does it mean we have to manually copy, paste, and adjust formats paragraph by paragraph? Of course not. This article will detail how to use Spire.Doc in a Python environment to efficiently achieve bidirectional conversion between Word and Markdown.

Why Choose Spire.Doc?

There are many document processing libraries on the market, but Spire.Doc excels in converting between Word and Markdown. It not only handles basic text content but also perfectly recognizes and converts complex structures such as headings, paragraphs, tables, and lists. More importantly, it supports both .doc and .docx Word formats as well as standard Markdown syntax. The converted documents are cleanly formatted and clearly structured, requiring almost no secondary adjustments.

Installing the Spire.Doc library:

pip install Spire.Doc

Word to Markdown: Three Lines of Core Code

from spire.doc import *
from spire.doc.common import *

# Create a Document object
document = Document()
# Load a Word file (supports .docx and .doc)
document.LoadFromFile("input.docx")

# Save as a Markdown file
document.SaveToFile("WordToMarkdown.md", FileFormat.Markdown)
document.Close()

After running the above code, all heading levels in the Word document are automatically mapped to Markdown's # to ###### tags, paragraphs retain appropriate line breaks, tables are converted to Markdown table syntax, and ordered/unordered lists are correctly recognized. The whole process takes only a few seconds, greatly improving work efficiency.

Markdown to Word: Equally Simple

The code structure for the reverse conversion is almost identical, with only the load and save formats swapped:

from spire.doc import *
from spire.doc.common import *

document = Document()
# Load a Markdown file
document.LoadFromFile("input.md")

# Save as a Word document (supports .docx and .doc)
document.SaveToFile("MdToDocx.docx", FileFormat.Docx)

# If you need the older .doc format, you can also save separately
# document.SaveToFile("MdToDoc.doc", FileFormat.Doc)
document.Close()

The converted Word document automatically applies default styles, with clear heading hierarchies, complete table borders, and reasonable list indentation, ready for printing or further typesetting.

Image Handling: The Base64 Embedding Problem and Solutions

In the process of converting Word to Markdown, there is an easily overlooked issue: image handling . By default, Spire.Doc converts embedded images in Word into Base64 encoding and directly embeds them into the Markdown file. The advantage of this approach is that the single file is self-contained, making it easy to share.

However, the downside is also obvious — when the document contains many high-resolution images, the Markdown file size can balloon dramatically, reaching tens or even hundreds of megabytes, causing editor lag and bloated Git repositories.

Optimization Solution: Extract Image for External References

A better approach is to extract images into a separate folder and then reference them via relative paths in Markdown. Although Spire.Doc does not directly provide a parameter to "automatically externalize image links when saving", we can solve this by manually extracting images and replacing references:

from spire.doc import *
import os

document = Document()
document.LoadFromFile("input.docx")

# Create a directory for images
image_dir = "images"
os.makedirs(image_dir, exist_ok=True)

# Iterate and extract all images
for i, image in enumerate(document.Images):
    with open(f"{image_dir}/img_{i}.png", "wb") as f:
        f.write(image.ImageData)

# First convert to Markdown (still Base64 embedded)
document.SaveToFile("temp.md", FileFormat.Markdown)

# Subsequently, use regex or string replacement to replace Base64 images with local path references
# This step needs to be done manually or with an additional script
document.Close()

If there are many documents, you can also fully automate the process: parse the generated Markdown file, locate Base64 image blocks, decode them and save locally, then replace them with the ![](images/img_x.png) format. Spire.Doc's document.Images collection provides us with the ability to extract images, and combined with scripting, full automation of this optimization can be achieved.

Practical Application Scenarios

This solution has been validated in multiple real-world scenarios:

Technical documentation migration : Batch convert existing Word-format product manuals to Markdown for import into VuePress or Docsify knowledge bases.
Multi-format publishing : Write in Markdown and convert to Word to meet client or supervisor requirements for formal document formats.
Collaborative review : Team members review using Word's "Track Changes" mode, then convert back to Markdown with one click to continue development.

Summary

With the Spire.Doc library, Python developers can achieve bidirectional conversion between Word and Markdown with just a few lines of code, while fully preserving core structures such as headings, paragraphs, tables, and lists. For documents with many images, combining image extraction with external reference solutions can effectively control file sizes and improve document management experience. If you often switch between the two formats, give this solution a try and bid farewell to tedious manual typesetting.