Mistral Document AI
Mistral Document AI is an enterprise-grade document processing solution that combines advanced Optical Character Recognition (OCR) with structured data extraction capabilities. It achieves over 99% accuracy in extracting and understanding complex text, handwriting, tables, and images from various documents across global languages. It can process up to 2,000 pages per minute on a single GPU, offering minimal latency and cost-efficient throughput. Mistral Document AI integrates OCR with powerful AI tooling to enable flexible, full document lifecycle workflows, making archives instantly accessible. It supports annotations, allowing users to extract information in a structured JSON format, and combines OCR with large language model capabilities to enable natural language interaction with document content. This allows for tasks such as question answering about specific document content, information extraction, and summarization, and context-aware responses.
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Mistral OCR 4
Mistral OCR 4 is a document extraction and understanding model built for enterprise search, RAG, domain-specific retrieval pipelines, and production-grade document intelligence. It extracts and structures content from a wide range of documents, moving beyond clean text and tables to return a structured representation of each page. Alongside extracted text, OCR 4 provides bounding boxes, typed-block classification, and inline confidence scores, helping downstream systems understand not only what the document says, but where each element sits, what role it plays, and how confident the model is in each region. Bounding boxes make in-context highlighting and reliable data pipelines possible, while block types and confidence scores support source-grounded citations, redactions, and human-in-the-loop verification. OCR 4 accepts common enterprise formats, including PDF, DOC, PPT, and OpenDocument, and supports 170 languages across 10 language groups.
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DeepSeek-OCR
DeepSeek-OCR is an open source model for Contexts Optical Compression, built to explore the boundaries of visual-text compression and investigate the role of vision encoders from an LLM-centric viewpoint. It is designed to compress long contexts through optical 2D mapping, using DeepEncoder as the core engine and DeepSeek3B-MoE-A570M as the decoder. DeepEncoder maintains low activations under high-resolution input while achieving high compression ratios, keeping the number of vision tokens manageable for document understanding. The model supports OCR and document parsing workflows for images and PDFs, with inference through vLLM or Transformers. Users can run image OCR with streaming output, process PDFs with high concurrency, or run batch evaluation for benchmarks. DeepSeek-OCR can convert documents to Markdown, perform free OCR without layouts, parse figures, describe images in detail, and locate referenced text inside an image.
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PrecisionOCR
PrecisionOCR is a ready-to-use, secure, HIPAA-compliant, cloud-based platform for extracting medical meaning from unstructured documents using Optical Character Recognition (OCR).
PrecisionOCR uses custom Optical Character Recognition and AI algorithms to convert PDFs/JPEGs/PNGs into structured, searchable documents. Organizations can work with our team to build OCR report extractors which look for specific types of information to extract or highlight to reduce the noise that comes from extracting all of the data within a document.
Natural language processing (NLP) and machine learning (ML) power the semi-automated and automated transformation of source material such as pdfs or images into structured data records that integrate seamlessly with EMR data using HL7s FHIR standards. Data can be automatically stored along side patient records.
Our OCR document classification is also available along with multiple ways to integrate including API and CLI support.
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