AI-Powered Text Extraction | 97% Accuracy | Multi-language Markdown Output
Based on DeepSeek Vision Language Model, ultra-low token consumption, free and open source with self-hosting capability
Upload images and experience the powerful capabilities of DeepSeek OCR in real-time
Or try sample images:
Recognition results will be displayed here
Compare DeepSeek-OCR with other mainstream OCR solutions in key performance indicators such as accuracy, efficiency, and deployment characteristics
| Model/Tool | Parameter Scale | Compression Support | Accuracy | Advantages | Disadvantages |
|---|---|---|---|---|---|
| 🚀 DeepSeek-OCR (Recommended) | 3B | Yes | 97% | Efficient, multi-language Markdown output | Non-deterministic, hardware dependent |
| 📊 GOT-OCR 2.0 | 约7B | No | 98%（无压缩） | High fidelity | High token consumption (60x) |
| 📄 MinerU 2.0 | 约10B | No | 95% | Powerful PDF processing | Slow speed (6000+ tokens/page) |
| ⚡ PaddleOCR | 轻量级 | No | 90% | Easy deployment | Weak structured output |
| 💬 ChatGPT (GPT-4o) | 闭源 | No | 约85%（OCR受限） | Easy to use | Short context, rejects long documents |
Everything you need to know about DeepSeek OCR
DeepSeek OCR 使用视觉语言模型（VLM）进行上下文感知 OCR，而 Tesseract 和 PaddleOCR 是传统的模式匹配引擎。主要区别：准确率 97% vs 85%，Token 效率 100 tokens/页 vs 更高处理开销。
分辨率模式在 token 消耗和准确率之间平衡：Tiny（64 tokens）- 简单文档；Small（100 tokens）- 推荐；Base（256 tokens）- 复杂布局；Large（400 tokens）- 高分辨率；Gundam - 学术论文。
是的，100% 开源！3B 参数模型在 GitHub 和 Hugging Face 上提供，采用宽松许可。您可以自托管、修改模型、无许可费商业使用。
最低：8GB 显存（RTX 3070）用于基本推理；推荐：16GB+ 显存（RTX 4090、A100-40G）用于生产环境；企业级：多 GPU 配置处理 20 万+ 页/天。