Best OCR API for extracting text from scanned PDFs

Combined ranking

1. 1
   Azure AI Document IntelligenceGrade ↗Visit ↗incumbent—19 pts

   GPT #2Claude #1Gemini #1Grok #1

   Best all-around accuracy on messy scans (skew, handwriting, low-DPI faxes) with layout, tables, and key-value extraction in one call; prebuilt models (invoices, receipts, IDs) cover most real workloads out of the box, and the Read/Layout tiers are cheap (~$1.50/1k pages) with strong multi-language support — assumption: the typical practitioner wants structured output from scanned business documents, not just raw text.

   + model takes & fixes− hide details

   Claude Best all-around accuracy on messy scans (skew, handwriting, low-DPI faxes) with layout, tables, and key-value extraction in one call; prebuilt models (invoices, receipts, IDs) cover most real workloads out of the box, and the Read/Layout tiers are cheap (~$1.50/1k pages) with strong multi-language support — assumption: the typical practitioner wants structured output from scanned business documents, not just raw text.

   Gemini Unmatched accuracy in layout parsing, table extraction, and dense form reading, with mature enterprise security. (Near-tie with Google Cloud Document AI, but edges it out due to superior table structure preservation and a more robust developer interface).

   Grok Highest accuracy on complex layouts, tables, forms, and scanned PDFs in benchmarks (often 90%+ field accuracy); strong custom model training, multilingual support, structured output for practitioners needing reliable extraction without heavy post-processing; integrates well for enterprise workflows.

   GPT Near-tied with Google for production OCR; strong multilingual and handwriting recognition, word coordinates and confidence, 2,000-page jobs, searchable-PDF output, broad regional availability, and an on-premises container option.

   Where it falls short

   per GPT Its product tiers, API versions, models, and separately billed enhancements are unusually confusing.

   per Claude Azure ecosystem friction — auth, SDK churn, and regional endpoint setup are heavier than a simple REST key, and complex custom extraction pushes you into pricier custom-model tiers.

   per Gemini High cost at scale and steep learning curve for custom models, making it unsuitable for small-scale developers or teams outside the Microsoft ecosystem.

   per Grok Higher cost for high-volume use and potential vendor lock-in; not ideal for fully offline or privacy-sensitive local-only deployments.

2. 2
   Google Cloud Document AIGrade ↗Visit ↗—16 pts

   GPT #1Claude #2Gemini #2Grok #3

   Excellent printed-text and handwriting accuracy, reading order, deskewing, confidence data, image-quality scoring, checkbox and math extraction; the strongest general-purpose managed choice, narrowly ahead of Azure.

   + model takes & fixes− hide details

   GPT Excellent printed-text and handwriting accuracy, reading order, deskewing, confidence data, image-quality scoring, checkbox and math extraction; the strongest general-purpose managed choice, narrowly ahead of Azure.

   Claude Top-tier raw OCR quality (inherits Vision engine) with the widest language coverage and excellent handwriting recognition; Layout Parser and specialized processors handle dense tables and forms well, and it integrates cleanly if you're already piping into BigQuery/GCS — near-tie with Azure, ranked second mainly on pricing complexity.

   Gemini Near-tie with Azure AI Document Intelligence. It stands out for its superior pre-trained processors (e.g., invoices, receipts, identity documents) requiring zero configuration, backed by a Google OCR engine highly resilient against noisy, low-resolution, or skewed scanned documents.

   Grok Strong overall text accuracy and multilingual capabilities, good speed, and competitive performance on general scanned PDFs; benefits from Google's AI research edge and easy integration for diverse document types.

   Where it falls short

   per GPT Google Cloud setup and processor/version management add complexity, and advanced OCR add-ons increase cost.

   per Claude Pricing is fragmented across processors and gets expensive fast for form/custom extraction; the processor/console model is confusing for first-time users versus a single OCR endpoint.

   per Gemini Returns a highly complex and deeply nested JSON payload (Document proto) that is difficult to parse and map, making it ill-suited for developers who need quick, clean text or markdown outputs.

   per Grok Sometimes trails Azure/Textract on highly structured data extraction (e.g., complex tables); pricing and occasional variability in layout understanding.

3. 3
   Amazon TextractGrade ↗Visit ↗—10 pts

   GPT #5Claude #3Gemini #4Grok #2

   Excellent for structured documents (forms, tables, invoices) with fast, scalable API performance and reliable text/handwriting extraction from scans; mature ecosystem and strong value for AWS users handling high throughput.

   + model takes & fixes− hide details

   Grok Excellent for structured documents (forms, tables, invoices) with fast, scalable API performance and reliable text/handwriting extraction from scans; mature ecosystem and strong value for AWS users handling high throughput.

   Claude Strongest table and form (key-value) extraction reliability at scale, mature async batch API for large PDF volumes, and Queries let you ask for specific fields without training; obvious pick when your stack is already on AWS with S3-based pipelines.

   Gemini A highly scalable, event-driven OCR API that integrates seamlessly with S3 and Lambda. Its Queries feature allows extracting specific data points using natural language without model training, making it ideal for AWS-native pipelines.

   GPT Reliable, inexpensive plain-text OCR with word geometry, asynchronous PDF processing, handwriting support, and optional extraction of tables, forms, queries, signatures, and expenses; particularly practical for AWS-native systems.

   Where it falls short

   per GPT Advanced structure features become expensive, and general multilingual and complex-layout performance is less compelling than the leaders.

   per Claude Weakest language coverage of the big three (heavily English/Latin-script biased) and per-feature pricing (tables + forms + queries stack) makes rich extraction notably pricier than plain text.

   per Gemini Substantially weaker layout parsing and text flow preservation for multi-column documents compared to Azure and LlamaParse, making it unsuitable for feeding text directly into LLM pipelines where reading order matters.

   per Grok Weaker than Azure on very complex or irregular layouts and line-item parsing in some tests; less flexible custom training.

4. 4
   PaddleOCRGrade ↗Visit ↗▲15 pts

   GPT #4Claude —Gemini #5Grok #4

   The strongest open-source contender, with capable PDF-to-JSON/Markdown pipelines, tables, formulas, layout parsing, 100-plus-language coverage, an official hosted API, and flexible self-hosting.

   + model takes & fixes− hide details

   GPT The strongest open-source contender, with capable PDF-to-JSON/Markdown pipelines, tables, formulas, layout parsing, 100-plus-language coverage, an official hosted API, and flexible self-hosting.

   Grok Leading open-source option with production-grade accuracy (often rivaling or beating cloud on many tasks via PP-OCR models), full PDF-to-structured data pipeline, multilingual, self-hostable, and free for high volume—ideal for practitioners prioritizing control, cost, and customization.

   Gemini The strongest open-source, self-hosted API option. It provides unmatched multilingual support (particularly for CJK languages), high execution speed, and low resource usage, entirely eliminating per-page processing fees and data privacy concerns.

   Where it falls short

   per GPT Production self-hosting demands substantially more infrastructure, tuning, and QA than a hyperscaler API, while the hosted service has a less mature global enterprise footprint.

   per Gemini Lacks out-of-the-box advanced semantic document layout understanding (such as converting complex multi-column structures or nested tables to clean markdown) without substantial custom pipeline development.

   per Grok Requires more setup/infra (GPU recommended for best speed/accuracy) and tuning compared to managed APIs; not as "plug-and-play" for beginners.

5. 5
   Mistral OCRGrade ↗Visit ↗incumbent▼15 pts

   GPT #3Claude #4Gemini —Grok —

   Outstanding value for complex PDFs, producing clean Markdown with tables, formulas, images, and document structure across many languages; especially attractive when OCR feeds RAG or LLM pipelines.

   + model takes & fixes− hide details

   GPT Outstanding value for complex PDFs, producing clean Markdown with tables, formulas, images, and document structure across many languages; especially attractive when OCR feeds RAG or LLM pipelines.

   Claude Best value of the LLM-native OCR wave — markdown-structured output (headings, tables, equations) that's ideal for RAG ingestion, strong on complex academic/technical layouts where classic engines fumble, at roughly $1/1k pages with a simple API; assumption: practitioner tolerates occasional generative quirks in exchange for structure-aware output.

   Where it falls short

   per GPT Generative document parsing is less deterministic than conventional OCR and is a poorer fit when every character, coordinate, and confidence score must be auditable.

   per Claude LLM-based extraction can hallucinate or silently drop text on degraded scans, and there are no per-word bounding boxes or confidence scores, so it's wrong for compliance/redaction workflows that need positional fidelity.

6. 6
   LlamaParseGrade ↗Visit ↗—3 pts

   GPT —Claude —Gemini #3Grok —

   The premier managed API for RAG and LLM-centric workflows. It processes complex scanned PDFs (multi-column layouts, nested tables, and embedded charts) and directly outputs structured, LLM-ready Markdown while maintaining reading order.

   + model takes & fixes− hide details

   Gemini The premier managed API for RAG and LLM-centric workflows. It processes complex scanned PDFs (multi-column layouts, nested tables, and embedded charts) and directly outputs structured, LLM-ready Markdown while maintaining reading order.

   Where it falls short

   per Gemini It is a closed, proprietary hosted API that raises data privacy concerns for sensitive documents, and its per-page pricing is prohibitively expensive for very high-volume, bulk archival processing.

7. 7
   SSuryaGrade ↗—1 pts

   GPT —Claude #5Gemini —Grok —

   The best open-source option in practice — modern deep-learning OCR with layout detection, reading order, and 90+ languages, and Marker turns scanned PDFs into clean markdown; self-hostable for privacy-sensitive documents (medical, legal) with accuracy that beats Tesseract decisively and approaches cloud APIs on clean scans.

   + model takes & fixes− hide details

   Claude The best open-source option in practice — modern deep-learning OCR with layout detection, reading order, and 90+ languages, and Marker turns scanned PDFs into clean markdown; self-hostable for privacy-sensitive documents (medical, legal) with accuracy that beats Tesseract decisively and approaches cloud APIs on clean scans.

   Where it falls short

   per Claude You run the infrastructure — GPU needed for reasonable throughput, and the GPL/commercial licensing split plus lower accuracy on badly degraded scans make it a poor fit for teams wanting a zero-ops API.

8. 8
   TTesseract OCRGrade ↗new1 pts

   GPT —Claude —Gemini —Grok #5

   Mature, widely supported open-source baseline with broad language support, lightweight for CPU, and battle-tested for basic scanned PDF text extraction in batch/offline scenarios; zero cost and full control.

   + model takes & fixes− hide details

   Grok Mature, widely supported open-source baseline with broad language support, lightweight for CPU, and battle-tested for basic scanned PDF text extraction in batch/offline scenarios; zero cost and full control.

   Where it falls short

   per Grok Weaker on complex layouts, handwriting, and noisy scans without heavy preprocessing/post-processing; lags behind modern DL options in raw accuracy.

Rank history

Just missed the top 5

By model

Common questions

Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand