{"slug":"best-ocr-api-for-extracting-text-from-scanned-pdfs","title":"Best OCR API for extracting text from scanned PDFs","question":"What are the best OCR APIs for extracting text from scanned PDFs in 2026?","verdict":"As of 2026-07-18, ChatGPT, Claude and Gemini collectively rank Azure AI Document Intelligence #1 for ocr api for extracting text from scanned pdfs on ModelsAgree. The models' case: Best all-around accuracy on messy scans (skew, handwriting, low-DPI faxes) with layout, tables, and key-value extraction in one call. The models' main caveat: Azure ecosystem friction — auth, SDK churn, and regional endpoint setup are heavier than a simple REST key, and complex custom extraction pushes you…. The strongest alternative is Google Cloud Document AI — Excellent printed-text and handwriting accuracy, reading order, deskewing, confidence data, image-quality scoring, checkbox and math extraction. Not unanimous: ChatGPT picks Google Cloud Document AI. Source: https://modelsagree.com/best/best-ocr-api-for-extracting-text-from-scanned-pdfs (modelsagree.com, CC BY 4.0).","category":"Docs AI","url":"https://modelsagree.com/best/best-ocr-api-for-extracting-text-from-scanned-pdfs","updated":"2026-07-18","models":["ChatGPT","Claude","Gemini"],"consensus":"2 of 3 models rank Azure AI Document Intelligence the top pick","disagreement":"ChatGPT picks Google Cloud Document AI","combined":[{"rank":1,"product":"Azure AI Document Intelligence","domain":"azure.microsoft.com","score":14,"appearances":3,"modelRanks":{"ChatGPT":2,"Claude":1,"Gemini":1},"reason":"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."},{"rank":2,"product":"Google Cloud Document AI","domain":"store.google.com","score":13,"appearances":3,"modelRanks":{"ChatGPT":1,"Claude":2,"Gemini":2},"reason":"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."},{"rank":3,"product":"Amazon Textract","domain":"amazon.com","score":6,"appearances":3,"modelRanks":{"ChatGPT":5,"Claude":3,"Gemini":4},"reason":"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."},{"rank":4,"product":"Mistral OCR","domain":"mistral.ai","score":5,"appearances":2,"modelRanks":{"ChatGPT":3,"Claude":4},"reason":"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."},{"rank":5,"product":"PaddleOCR","domain":null,"score":3,"appearances":2,"modelRanks":{"ChatGPT":4,"Gemini":5},"reason":"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."},{"rank":6,"product":"LlamaParse","domain":"llamaindex.ai","score":3,"appearances":1,"modelRanks":{"Gemini":3},"reason":"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."},{"rank":7,"product":"Surya","domain":null,"score":1,"appearances":1,"modelRanks":{"Claude":5},"reason":"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."}],"perModel":{"ChatGPT":[{"rank":1,"product":"Google Cloud Document AI","reason":"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.","fix":"Google Cloud setup and processor/version management add complexity, and advanced OCR add-ons increase cost."},{"rank":2,"product":"Azure AI Document Intelligence","reason":"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.","fix":"Its product tiers, API versions, models, and separately billed enhancements are unusually confusing."},{"rank":3,"product":"Mistral OCR","reason":"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.","fix":"Generative document parsing is less deterministic than conventional OCR and is a poorer fit when every character, coordinate, and confidence score must be auditable."},{"rank":4,"product":"PaddleOCR","reason":"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.","fix":"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."},{"rank":5,"product":"Amazon Textract","reason":"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.","fix":"Advanced structure features become expensive, and general multilingual and complex-layout performance is less compelling than the leaders."}],"Claude":[{"rank":1,"product":"Azure AI Document Intelligence","reason":"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.","fix":"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."},{"rank":2,"product":"Google Cloud Document AI","reason":"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.","fix":"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."},{"rank":3,"product":"Amazon Textract","reason":"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.","fix":"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."},{"rank":4,"product":"Mistral OCR","reason":"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.","fix":"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."},{"rank":5,"product":"Surya","reason":"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.","fix":"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."}],"Gemini":[{"rank":1,"product":"Azure AI Document Intelligence","reason":"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).","fix":"High cost at scale and steep learning curve for custom models, making it unsuitable for small-scale developers or teams outside the Microsoft ecosystem."},{"rank":2,"product":"Google Cloud Document AI","reason":"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.","fix":"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."},{"rank":3,"product":"LlamaParse","reason":"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.","fix":"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."},{"rank":4,"product":"Amazon Textract","reason":"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.","fix":"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."},{"rank":5,"product":"PaddleOCR","reason":"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.","fix":"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."}]},"missedByModel":{"ChatGPT":[{"product":"ABBYY Cloud OCR SDK","reason":"mature, accurate multilingual OCR and searchable-PDF conversion, but opaque sales-led pricing and an aging developer experience weaken its typical-practitioner value"},{"product":"Mathpix Convert API","reason":"exceptional for scientific papers, equations, and tables, but too specialized to outrank broader document OCR services"}],"Claude":[{"product":"ABBYY Vantage","reason":"still excellent accuracy on degraded scans and the standard in legacy enterprise capture, but enterprise sales motion and pricing make it poor value versus cloud APIs for a typical developer"}],"Gemini":[{"product":"Unstructured API","reason":"its raw OCR accuracy and table extraction on low-quality scanned PDFs falls behind specialized layout parsers like LlamaParse or Azure"},{"product":"Mindee","reason":"it lacks the general-purpose layout parsing power and custom modeling capabilities of Azure or AWS for complex, non-standard PDFs"}]}}