Run a phone photo of a crumpled receipt through a plain OCR engine and you get a wall of characters with the totals in the wrong spots. Useless. That gap, between reading the text and actually understanding the document, is what splits the seven tools here.
Some you host yourself for free. Some are cloud APIs built for whatever stack you're already on. One reads the layout first and hands back real structure. That's how the list is sorted, and every tool gets a snippet you can paste and run.
Agentic extraction: reading beyond OCR
LandingAI's Agentic Document Extraction (ADE)
LandingAI's Agentic Document Extraction (ADE) isn't really an OCR engine, and that's the point. Plain OCR turns pixels into characters. ADE reads the layout first, works out what each block is, and hands you structured data an agent can just use.
Parse reads a document down to its lines and table cells, and grounds every one of them: each value carries its own page, character range, and bounding box, so it points straight back to where it sits on the page. What comes back is reading-order Markdown plus a structured tree of pages, elements, lines, and cells, each with a stable id. Feed that Markdown to Extract, and typed JSON comes back with every value still traceable to its page.
On the infrastructure side, ADE ships Python and TypeScript SDKs and a CLI, plus an async Jobs API for large batches, and a single document can run up to 6,000 pages or 1 GB. Credits run at a flat $1 for every 100, across every tier. For a regulated shop, there's HIPAA-compliant processing with a BAA and a Zero Data Retention option starting at the Team tier, and VPC or on-premises deployment on Enterprise.
Reach for it when you need structured, checkable output from messy documents, not raw text you'll spend a day reshaping.
Install with pip install landingai-ade. A minimal Parse flow, submitting a job and reading back the result, looks like this:
from landingai_ade import LandingAIADE
client = LandingAIADE() # reads VISION_AGENT_API_KEY
job = client.v2.parse_jobs.create(
document_url="https://your-storage.example.com/scanned-invoice.pdf",
service_tier="standard",
)
done = client.v2.parse_jobs.wait(job.job_id, raise_on_failure=True)
# done.result.markdown holds the reading-order Markdown, grounded per element
print(done.result.markdown)
Managed cloud OCR APIs
Mistral OCR
Mistral OCR is a vision language model that reads a page and gives you Markdown back, with image bounding boxes and structure metadata. It handles 40 plus languages and takes PDFs and the usual image formats, and because the output is Markdown per page, it drops into an LLM pipeline with barely any cleanup. It's a hosted call to Mistral, and it's one of the cheaper managed options per page.
Reach for it when you've got multilingual docs and you care more about clean Markdown than where it runs. Install with pip install mistralai. One call, page Markdown out:
from mistralai import Mistral
client = Mistral(api_key="YOUR_KEY")
ocr_response = client.ocr.process(
model="mistral-ocr-latest",
document={"type": "document_url", "document_url": "https://example.com/doc.pdf"},
)
print(ocr_response.pages[0].markdown)
AWS Textract
If you're already on AWS, Textract is the path of least resistance. Its detect_document_text call reads printed and handwritten text and gives you lines and words with coordinates, and when you need forms and tables too, analyze_document covers that at a higher price. Pricing runs per feature and per page, so a plain text call costs less than a forms-and-tables one.
Reach for it when you want dependable text plus location data inside an AWS pipeline. It runs through boto3, and the pure OCR call is detect_document_text:
import boto3
client = boto3.client("textract")
with open("scanned-form.png", "rb") as f:
resp = client.detect_document_text(Document={"Bytes": f.read()})
for block in resp["Blocks"]:
if block["BlockType"] == "LINE":
print(block["Text"])
Azure AI Document Intelligence
Azure AI Document Intelligence is the old Form Recognizer, renamed. Its prebuilt-read model pulls printed and handwritten text out of PDFs and scans, and it reads Word, Excel, PowerPoint, and HTML too, tracking paragraphs, lines, words, and languages along the way. It runs as a cloud call or as an on-premises Docker container, which matters when your data can't leave the building, and the same Read engine underlies Azure's Layout, Invoice, and Receipt models, so moving up to structure later is a small step rather than a rebuild.
Reach for it when you're on Azure and want OCR with a clear path to prebuilt document models. Install with pip install azure-ai-documentintelligence. Point prebuilt-read at a file:
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.core.credentials import AzureKeyCredential
client = DocumentIntelligenceClient(
endpoint="https://<resource>.cognitiveservices.azure.com/",
credential=AzureKeyCredential("YOUR_KEY"),
)
with open("scanned-doc.pdf", "rb") as f:
poller = client.begin_analyze_document("prebuilt-read", body=f)
print(poller.result().content)
Google Cloud Vision
Google Cloud Vision is Google's general image API, and OCR is one feature among many. DOCUMENT_TEXT_DETECTION handles dense and handwritten pages and returns them as pages, blocks, paragraphs, words, and symbols with bounding boxes, and it figures out the language on its own. Pricing runs per image and per feature, with the first 1,000 units a month free and a flat rate after that.
Reach for it when you're on GCP and want reliable OCR with plenty of per-word metadata to build on. Install with pip install google-cloud-vision. Document mode handles dense and handwritten pages:
from google.cloud import vision
client = vision.ImageAnnotatorClient()
with open("handwritten-note.png", "rb") as f:
image = vision.Image(content=f.read())
response = client.document_text_detection(image=image)
print(response.full_text_annotation.text)
Open source OCR engines
Tesseract OCR
Tesseract is the old reliable: open source, Apache 2.0, backed by Google, and able to read 100 plus languages entirely on your own hardware. You get plain text out of the box, with hOCR and TSV available if you want word positions. It nails clean scans. Throw handwriting, tables, or a bad photo at it, though, and you're writing the cleanup yourself.
Reach for it when you want free, self-hosted OCR and your documents are reasonably tidy. Install the Tesseract binary, then pip install pytesseract pillow:
import pytesseract
from PIL import Image
text = pytesseract.image_to_string(Image.open("scan.png"))
print(text)
PaddleOCR
PaddleOCR is Baidu's open source OCR toolkit, also Apache 2.0. It turns PDFs and images into structured, LLM-ready JSON or Markdown, and it reads a lot of languages. PP-OCR pairs detection with recognition, and the PP-Structure pipeline adds layout, tables, and reading order on top. It runs locally on CPU or GPU, which makes it a fit for self-hosted and air-gapped setups.
Reach for it when you want open source OCR that keeps structure, especially for multilingual or Chinese text. Install PaddlePaddle, then pip install paddleocr:
from paddleocr import PaddleOCR
ocr = PaddleOCR(lang="en")
result = ocr.predict("scan.png")
print(result)
How the seven compare
| Tool | Text type | Languages | Output | Deployment | Cost model |
|---|---|---|---|---|---|
| LandingAI ADE | Print, handwriting, scans | Broad, incl. non-Latin | Markdown + structured JSON | Cloud, VPC, on-prem | Credit-based |
| Mistral OCR | Print, scans | 40+ | Markdown | Managed cloud | Per page |
| AWS Textract | Print, handwriting | Broad | Text + blocks | AWS cloud | Per feature, per page |
| Azure AI Document Intelligence | Print, handwriting | Broad | Text + layout | Cloud, on-prem container | Per page |
| Google Cloud Vision | Print, handwriting | Broad | Text + blocks | Cloud, on-prem option | Per image, per feature |
| Tesseract OCR | Print, clean scans | 100+ | Plain text | Self-hosted | Free |
| PaddleOCR | Print, scans | Many | JSON, Markdown | Self-hosted | Free |
Matching the tool to the job
The right pick follows from what you're actually optimizing for. If you're pulling structured data out of messy or regulated documents, LandingAI ADE is the one built for it: you get grounded structure instead of raw text to reshape yourself. If the job is clean Markdown from multilingual pages, Mistral OCR does that with the least friction.
If you're running OCR inside a cloud you're already committed to, stay there: Textract on AWS, Azure AI Document Intelligence on Azure, Cloud Vision on GCP. And if the requirement is free, self-hosted, or air-gapped, Tesseract handles clean scans well, while PaddleOCR is the better choice once you also need layout and tables preserved.
Whatever you land on, test it against your worst documents, not the clean sample in the docs. The photographed receipt, the multi-column scan, the handwritten form: that's where these tools actually fall apart, and it's the part that bites you later if you skip it now.
OCR versus document extraction, and a few practical differences
OCR turns pixels into characters. Extraction goes further and reads layout, tables, and fields, then hands back structured data. ADE sits on the extraction side of that line; Tesseract and Cloud Vision sit closer to plain OCR.
Handwriting support varies more than people expect. Textract, Azure AI Document Intelligence, and Cloud Vision all read it reasonably well. Tesseract is built for printed text and does a poor job on handwriting.
Running fully offline is possible with a few of these. Tesseract and PaddleOCR run entirely on your own hardware. Azure and Google both offer on-premises container options, and ADE offers VPC and on-premises deployment on its Enterprise tier.
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