Comparing the Top 6 OCR Models in 2025
Optical Character Recognition (OCR) technology has advanced beyond simple text extraction into comprehensive document intelligence. Modern OCR systems handle both scanned and digital PDFs in one pass, preserving layout, detecting tables, and extracting key-value pairs while supporting multiple languages. Many teams now seek OCR solutions able to integrate directly with Retrieval-Augmented Generation (RAG) and agent pipelines.
Leading OCR Systems in 2025
Six main OCR solutions cover most real-world use cases:
- Google Document AI
- AWS Textract
- Microsoft Azure OCR
- ABBYY
- PaddleOCR
- DeepSeek OCR
The goal is not to rank these systems by one metric, as each targets different requirements. Instead, this comparison helps choose the best OCR based on document volume, deployment method, languages supported, and AI integration.
Google Document AI
This enterprise solution processes PDFs and images, whether scanned or digital, extracting text with preserved layout, tables, key-value pairs, and selection marks. It supports handwriting recognition in 50 languages and detects math expressions and font styles. This is especially useful for financial statements, educational forms, and archives. The output is structured JSON, compatible with Vertex AI or any RAG system. It's ideal when data already resides on Google Cloud or when layout preservation is crucial for subsequent large language model (LLM) stages.
AWS Textract
Textract offers two API options: synchronous for small documents and asynchronous for large multipage PDFs. It extracts text, tables, form data, and signatures, returning results as blocks with relationships.
“Textract provides two API lanes, synchronous for small documents and asynchronous for large multipage PDFs. It extracts text, tables, forms, signatures and returns them as blocks with relationships.”
It fits workloads requiring scalable processing and complex document structures.
Summary
Choosing the right OCR system depends on your specific document types, processing volume, language needs, deployment preferences, and how the extracts will feed into AI workflows.
Author's summary: Modern OCR systems in 2025 are essential for comprehensive document understanding, supporting complex layouts, multiple languages, and integration with AI workflows, with key players catering to different use cases.