Description
The paper introduces DeepSeek-OCR, a vision-language model designed to go beyond traditional OCR by extracting text from images and understanding visual context such as layout, semantics, and multimodal relationships. It presents a mixed-expert architecture where only relevant sub-modules activate, enabling efficient processing of high-resolution document images and complex visuals. The authors describe their training system, datasets spanning screenshots, PDFs, and scene text, and evaluate the model on benchmark tasks where it demonstrates strong performance in text extraction, document understanding and layout reasoning. The paper concludes that the unified vision + language approach offers improved accuracy and flexibility for modern OCR applications.
