DeepSeek OCR is a two-stage transformer-based document AI system that utilizes context optical compression to deliver state-of-the-art document intelligence. It compresses high-resolution documents into lean vision tokens, then decodes them with a 3B-parameter mixture-of-experts model to achieve near-lossless text, layout, and diagram understanding across 100+ languages. It supports GPU-efficient throughput for complex layouts and is trained on 30 million real PDF pages plus synthetic data, preserving layout structure, tables, chemistry (SMILES strings), and geometry tasks.
Next-gen document intelligence with context optical compression and multilingual support.
- Compressing scanned books and reports for downstream search, summarization, and knowledge graphs.
- Extracting geometry reasoning, engineering annotations, and chemical SMILES from technical diagrams and formulas.
- Building global corpora across 100+ languages for multilingual dataset creation.
- Embedding into invoice, contract, or form-processing platforms for layout-aware JSON and HTML output.
- DeepSeek OCR can be used in three main ways: 1. Deploy locally with GPUs by cloning the GitHub repo
- downloading the 6.7 GB checkpoint
- and configuring PyTorch. 2. Call DeepSeek OCR via its OpenAI-compatible API endpoints to submit images and receive structured text. 3. Integrate DeepSeek OCR into existing workflows by converting OCR outputs to JSON
- linking SMILES strings to cheminformatics pipelines
- or auto-captioning diagrams.
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