Best Document AI Platforms in 2026
A hands-on evaluation of platforms for intelligent document processing, including OCR, layout analysis, table extraction, and document search. Tested on invoices, contracts, and technical manuals.
How We Evaluated
Extraction Accuracy
Quality of text extraction, table parsing, and layout understanding across diverse document types.
Document Type Coverage
Range of supported formats (PDF, DOCX, images, scans, handwritten) and specialized templates.
Search & Retrieval
Quality of document search after processing, including semantic search and structured extraction.
Integration & Scale
API design, throughput for batch processing, and integration with downstream workflows.
Mixpeek
Multimodal document processing platform that combines OCR, layout analysis, and semantic understanding. Processes PDFs alongside images and other modalities in unified pipelines with advanced retrieval.
Pros
- +Processes PDFs, images, and scanned documents in one pipeline
- +Semantic search across document content with ColBERT retrieval
- +Cross-modal queries (find documents by image content)
- +Self-hosted deployment for sensitive document workloads
Cons
- -Not specialized for forms or invoice extraction
- -Requires pipeline setup for specific document types
- -No built-in template-based extraction
Google Document AI
Google Cloud's document processing service with pre-trained processors for common document types. Offers OCR, form parsing, and specialized processors for invoices, receipts, and contracts.
Pros
- +Excellent OCR accuracy including handwritten text
- +Pre-trained processors for common document types
- +Good table and form field extraction
- +Integrates with BigQuery and Cloud Storage
Cons
- -Vendor lock-in to Google Cloud
- -Custom processor training requires significant labeled data
- -Limited semantic search capabilities
- -Per-page pricing can be expensive for large archives
AWS Textract
Amazon's document analysis service for extracting text, tables, and forms from scanned documents. Part of the broader AWS AI suite with good integration into Lambda-based workflows.
Pros
- +Strong table extraction from complex documents
- +Good handwriting recognition
- +Queries feature for targeted data extraction
- +Integrates well with AWS Lambda and S3
Cons
- -Limited layout understanding for complex documents
- -No built-in semantic search or RAG support
- -Custom model training not available
- -Pricing per page at scale can be significant
Unstructured
Open-source document parsing library and API that converts PDFs, DOCX, HTML, and images into structured chunks for downstream AI pipelines. Strong at preparing documents for RAG applications.
Pros
- +Open-source core with broad format support
- +Good chunking strategies for RAG applications
- +Preserves document hierarchy and metadata
- +Active community and regular updates
Cons
- -OCR accuracy lower than specialized services
- -No built-in search or retrieval
- -Complex document layouts can be challenging
- -Requires separate vector database for search
Azure AI Document Intelligence
Microsoft's document processing service (formerly Form Recognizer) with pre-built and custom models for extracting structured data from documents, forms, and receipts.
Pros
- +Strong pre-built models for invoices and receipts
- +Custom model training with few labeled samples
- +Good integration with Microsoft 365 ecosystem
- +Layout API preserves reading order
Cons
- -Azure ecosystem dependency
- -Limited multimodal capabilities beyond documents
- -Custom model training UI can be clunky
- -Concurrent processing limits on lower tiers
Frequently Asked Questions
What is the difference between OCR and Document AI?
OCR (Optical Character Recognition) converts images of text into machine-readable text. Document AI goes further by understanding document layout, extracting structured data from tables and forms, classifying document types, and enabling semantic search over document content. Think of OCR as 'reading the text' and Document AI as 'understanding the document.'
How accurate is AI document extraction for handwritten text?
Modern AI achieves 85-95% accuracy on printed handwritten text in clear conditions. Accuracy drops for cursive handwriting, poor scan quality, or unusual formats. Google Document AI and Azure AI Document Intelligence tend to perform best on handwriting. For critical applications, always include a human review step for low-confidence extractions.
Can Document AI handle documents in multiple languages?
Most platforms support 50+ languages for OCR, with the best accuracy for Latin-script languages. CJK (Chinese, Japanese, Korean) support varies. Arabic and right-to-left scripts are supported but sometimes with lower accuracy. For multilingual document archives, test with representative samples in each language before committing to a platform.
How do I build document search after extraction?
After extracting text and structure, you need to generate embeddings and store them in a vector database. End-to-end platforms like Mixpeek handle this automatically. With standalone tools like Unstructured or Textract, you will need to: chunk the extracted text, generate embeddings with a model like E5 or OpenAI, store them in a vector database, and build a retrieval layer.
Ready to Get Started with Mixpeek?
See why teams choose Mixpeek for multimodal AI. Book a demo to explore how our platform can transform your data workflows.
Explore Other Curated Lists
Best Multimodal AI APIs
A hands-on comparison of the top multimodal AI APIs for processing text, images, video, and audio through a single integration. We evaluated latency, modality coverage, retrieval quality, and developer experience.
Best Video Search Tools
We tested the leading video search and understanding platforms on real-world content libraries. This guide covers visual search, scene detection, transcript-based retrieval, and action recognition.
Best AI Content Moderation Tools
We evaluated content moderation platforms across image, video, text, and audio moderation. This guide covers accuracy, latency, customization, and compliance features for trust and safety teams.