Deepseek OCR

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.

## How to Use

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.

Deepseek OCR's

## Key Features

- Context Optical Compression Engine - Multilingual Support (100+ languages) - Structured Output (HTML, Markdown, SMILES, JSON) - GPU-efficient throughput (200k pages/day on A100) - High precision (97% exact-match accuracy) - MIT-licensed weights for on-premises deployment

## Use Cases

- 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.