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DeepSeek-OCR is an open-source optical character recognition solution built as part of the broader DeepSeek AI vision-language ecosystem. It is designed to extract text from images, PDFs, and scanned documents, and integrates with multimodal capabilities that understand layout, context, and visual elements beyond raw character recognition. The system treats OCR not simply as “read the text” but as “understand what the text is doing in the image”—for example distinguishing captions from body text, interpreting tables, or recognizing handwritten versus printed words. It supports local deployment, enabling organizations concerned about privacy or latency to run the pipeline on-premises rather than send sensitive documents to third-party cloud services. The codebase is written in Python with a focus on modularity: you can swap preprocessing, recognition, and post-processing components as needed for custom workflows.
Features
- Modular pipeline architecture for image preprocessing, text recognition, and layout analysis
- Support for both printed and handwritten text across multiple scripts and languages
- Table and chart recognition so structured content is preserved, not just linear text
- Local-deployment option to keep data on-premises and avoid cloud transfers
- Python API and CLI tool for integration into scripts, workflows, or batch jobs
- Configurable post-processing (e.g., spell checking, layout repair, structured output)
Project Samples
