OCR vs ADE: Mechanisms Behind the Methods

Introduction

In today’s data-driven world, there is a heavy reliance on extracting information from physical sources and interpreting their content. When we talk about extracting data from documents, files, and other sources, there are two approaches that stand out among others. Optical Character Recognition (OCR) and Agentic Document Extraction (ADE) are two methods with distinct capabilities.

The principles of these two mechanisms differ, and understanding their applications helps you leverage them effectively. In a nutshell, OCR displays a visual representation of texts and converts them into readable characters. ADE, on the other hand, employs a more complex approach to representing data, extracting structured data from a wide range of unstructured inputs.

What is Optical Character Recognition (OCR)?

OCR is an effective method for interpreting text in images and files. This method involves certain mechanisms, like image processing and character extraction. Although it was designed to digitize books and newspapers, OCR has evolved into a vital technology in scanning, document processing, and machine learning applications.

With the capability of identifying and converting printed, handwritten, or raw image data into readable text, OCR relies on several stages of processing. It first employs image processing on the input data. This step helps reduce lines and segment text from other elements, providing the recognition engine with a clean image to work with.

OCRs also perform feature extraction to create distinct attributes within every character, making it possible to analyze various texts. Modern OCR models now employ CNN architecture to make feature extraction more seamless. And finally, it uses a post–processing technique that provides more context to the words identified to avoid errors. For example, misreading “rn” as “m” can be corrected by analyzing word probability.

What is Agentic Document Extraction?

In contrast to OCR, Agentic Document Extraction provides more data when parsing through files and images. It can even provide context to the data. One of the reasons behind ADE’s robust output is that it can read more than just plain text; ADE is also capable of identifying tables, forms, and so on.

ADE can extract structured data from unstructured documents, files, and images. It performs this process by identifying tables, forms, and texts in a document, while also understanding the context within each element. Agentic Document Extraction can parse your files and return the data in hierarchical JSON format with its capabilities.

What features make ADE so robust and effective? ADE leverages several vital capabilities to execute document extraction tasks. These include the following:

Element Detection: ADE can identify and segment regions of a document with form fields, tables, checkboxes, and text.
Recognizes structured order: Each element in a document has a distinct structure, and ADE has an effective understanding of the relationship between texts, lines, and captions present in different regions around the document.
Visual Grounding: The JSON output tells you where each piece of information came from in the document. It also says what page and where on the page. This method makes it easy to double-check and track records.
Flexible Output: ADE’s ability to return results in Markdown and JSON makes it ready for use in RAG applications and workflows.

OCR vs ADE: Working principle

The approach and output of OCR and ADE differ significantly. While OCR specializes in recognizing characters from images and documents, ADE provides more output data by extracting a structured representation of text and other elements.

OCR’s method starts with image processing. This stage ensures that the document goes through a ‘noise reduction’ technique to give a clean image before segmenting the text regions.

There is a different technique ADE employs in its tasks. It uses data parsing to break down the inputs into logical structures. It then uses a blend of pattern-based methods (regex, templates) and AI-driven extraction (NLP, NER) to pull out key fields such as invoice numbers, names, or dates.

In essence, OCR is the foundation for converting raw images into text, while ADE builds on top of that (or directly from digital text) to deliver actionable, validated, and structured information. OCR answers the “what characters are on this page?” question, whereas ADE answers “what does this document mean, and how do we use its contents in business workflows?”

OCR vs ADE: Comparative Analysis

Let’s put into practice what these systems can carry out. Firstly, we will explore Landing AI’s Agentic Document Extraction using the agentic_doc Python library. We will run ADE on a payment report from Bolt —a ride-hailing app- and see how this software solution understands the visual representation of the document and its context.

So, let’s use the agentic_doc library to extract information from the PDF file above.

Prerequisites

A Google Colab Environment
Vision agent API Key and
A sample file to extract data from, i.e, jpeg, pdf, png..

Install dependencies

`!pip install agentic-doc`

The agentic-doc is the Python library that wraps Landing AI’s ADE. So, it is a compulsory package to have in your environment.

Parsing the document

from agentic_doc.parse import parse


# Parse a local file
result = parse("/content/NG1124-459199.pdf")


# Get the extracted data as markdown
print("Extracted Markdown:")
print(result[0].markdown)


# Get the extracted data as structured chunks of content in a JSON schema
print("Extracted Chunks:")
print(result[0].chunks)

The parse function reads the file path “/content/NG1124-459199.pdf” and returns a result that contains a structured output of the data in the document. The result in the document is then converted into a clean, readable markdown text, and finally, result[0].chunks displays the content as structured JSON-like chunks, making it easy to work with specific sections.

Here is the unaltered output appearance on Google Colab.

The output in readable text shows that ADE understands every section and element in the document.

OCR, on the other hand, cannot provide this much context to a document. We will run a file through an OCR model to demonstrate its functionality.

How to Run an OCR Model

Many OCR models work by displaying the text and highlighting it with bounding boxes. Others are only capable of recognizing single-line text in files and even handwritten images. TrOCR is a good example of this; others include easyocr, PaddleOCR, and others.

Here is a simple illustration of how this works.

Prerequisite

A Google Colab environment.
Install dependencies

Installing Dependencies

pip install torch torchvision torchaudio
pip install transformers
pip install pillow
pip install requests

You can run the model using this code;

from transformers import TrOCRProcessor, VisionEncoderDecoderModel

from PIL import Image

import requests


# load image from the IAM database (actually, this model is meant to be used on printed text)

url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'

image = Image.open(requests.get(url, stream=True).raw).convert("RGB")


processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-printed')
model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-printed')
pixel_values = processor(images=image, return_tensors="pt").pixel_values

Generating the results

generated_text

This shows the text in the image that was handwritten.

OCR vs ADE: Key Takeaway

While OCR and ADE operate differently, their real power emerges when used together. In enterprise settings like invoice processing, OCR first transforms a scanned PDF into readable text. This step converts static images into digital characters, creating the foundation for further analysis.

ADE then takes over, identifying key details such as vendor names, invoice numbers, dates, and totals. Once extracted, the data is validated and seamlessly entered into systems like ERPs. This synergy creates a smooth automation pipeline, reducing manual effort while ensuring accuracy at scale.

Wrapping Up

These two software solutions complement each other regardless of their varying purposes and capabilities. So, it is essential to know how to use them independently or together.