DeepSeek-OCR is an optical character recognition model that investigates the role of vision encoders from a language model-centric viewpoint.
The tool addresses the challenge of efficiently extracting and compressing visual information for language models. It operates by encoding images at multiple resolution levels—ranging from tiny 512×512 inputs to large 1280×1280 inputs—and supports dynamic resolution modes that combine multiple scales in a single pass. This approach allows the model to balance visual detail capture with computational efficiency, producing compressed visual-text representations suitable for downstream language model processing.
Developers should choose this tool if they need to process images through language models with controlled token budgets and variable input resolutions. The project suits workflows involving document analysis, PDF processing, and batch evaluation on benchmarks. The tool is officially supported in upstream vLLM, which provides inference optimization and enables concurrent processing at approximately 2500 tokens per second on high-end hardware. Both vLLM and Transformers inference backends are available, giving flexibility in deployment choices.
The project maintains active development with official integration into vLLM's upstream documentation and examples. The codebase provides configuration-driven inference pipelines for both image and PDF inputs, with support for streaming output modes. A research paper accompanies the release, documenting the model's design and approach to visual-text compression.
