MiniCPM-OCR-1.0

MiniCPM-OCR 1.0 (1.3B) is the first OCR-specific model in the MiniCPM-V/o family. It focuses on text-centric document understanding — text, tables, formulas, charts, seals, and structured information extraction — and balances quality against speed on real-world captures.

Highlights

• 🏆 SOTA document parsing: 93.68 on Real5-OmniDocBench, a benchmark for real-world document parsing, with leading results on OmniDocBench v1.6, Wild-OmniDocBench, and olmOCR-Bench. Handles Chinese and English documents and PDFs, including scans, curved pages, photographs, skewed pages, and recaptured documents.
• ⚡ Efficient inference. With DSpark decoding acceleration (BF16, GGUF) end-to-end processing efficiency improves by 55%. Throughput is about 1.49× PaddleOCR-VL-1.6 and 3.62× MinerU2.5 Pro.
• 🔍 General recognition and information extraction: 54.7 on OCRBench v2, plus key information extraction (KIE) for downstream automation.
• 🛠️ Easy to deploy and develop: vLLM, SGLang, llama.cpp, and Ollama from day one, with quantized variants and multi-chip builds. Supports further fine-tuning, and ships a pip SDK, a full API, and agent workflows. Online demos: HF Spaces · CN · Official CN · Official Global.

SOTA quality at higher throughput

Among the open-source OCR models compared, MiniCPM-OCR 1.0 reaches 1.2 pages/s and the highest equal-weight average score across OmniDocBench v1.6, Real5-OmniDocBench, Wild-OmniDocBench, and olmOCR-Bench.

_{Average score is the equal-weight mean of the four benchmarks. All models use accelerated configurations. Settings and reproduction: reproduction guide. Per-model throughput: model guide.}

Benchmark comparisons

Full MiniCPM-OCR 1.0 evaluation results

Each panel keeps its own benchmark's scores; higher is better. Scores from different benchmarks are not directly comparable. Test settings and reproduction steps are in the reproduction guide; per-benchmark discussion is in the model guide.

Throughput

On an NVIDIA GeForce RTX 5090 with all 1651 OmniDocBench v1.6 images, image-level concurrency 1, and each system's accelerated configuration, MiniCPM-OCR 1.0 reaches 1.2 pages/s (relative throughput 1.00×; higher is faster).

Concurrency 1 is image-level: regions inside a page are still recognised in parallel. Real throughput also depends on page content, backend, concurrency, and hardware.

Examples

Left to right: original document, detected layout, and rendered Markdown.

English documents

Chinese documents

Model architecture

See the model guide for architecture, parsing workflow, deployment options, and benchmark breakdowns.

Quick start

Online Demo

To quickly try text-centric document understanding and structured information extraction, we recommend HF Spaces, CN, Official CN, Official Global / API.

Local deployment

For local deployment of MiniCPM-OCR, we recommend our SDK (tutorial) with DSpark enabled by default. For document parsing task, It reads a PDF or image, detects layout with PP-DocLayoutV3, recognises each region, and assembles Markdown in reading order. Our SDK supports different inference backends, including vLLM, SGLang, llama.cpp, Ollama, Transformers, and FlagOS.

Agent Skill

You can also have a coding agent follow the Agent Skill to set up the environment, download the model, and parse a first document.

Read and execute https://github.com/OpenBMB/MiniCPM-OCR/blob/main/docs/SKILL_en.md

Fine-tuning

LLaMA-Factory · ms-swift — data preparation, full-parameter SFT with the multimodal projector frozen, training, and evaluation (French handwritten text lines).

Recognition guide

Related checkpoints

DSpark requires a matching main and draft model from the same release — do not mix draft weights with a different main checkpoint. See DSpark acceleration.

For OCR quality assessment (auto-accept, human-review routing, best-of-N across models), see MiniCPM-OCR Jev.

FAQ

Installation, model downloads, GPU memory, input formats, and unexpected outputs: FAQ.

License

Model weights, code, and documentation are released under the Apache License 2.0.