MiniCPM-OCRNEW

MiniCPM-OCR is a family of models for efficient document parsing and text recognition. MiniCPM-OCR 1.0 parses Chinese and English documents and PDFs, balancing quality and efficiency across challenging inputs such as scans, photographs, curved pages, and recaptured documents. MiniCPM-OCR Jev evaluates OCR results at the region level, distinguishing problematic regions from reliable ones. It works with different parsing models to help users locate content that needs review.

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, covering text, tables, formulas, charts, seals, and structured information extraction. Its key features include:

• 🏆 SOTA document parsing: MiniCPM-OCR 1.0 achieves a SOTA score of 93.68 on Real5-OmniDocBench, a benchmark for real-world document parsing, and demonstrates leading parsing capabilities on OmniDocBench v1.6, Wild-OmniDocBench, and olmOCR-Bench. It supports Chinese and English documents and PDFs, handling both standard documents and challenging capture conditions, including scans, curved pages, photographs, skewed pages, and recaptured documents.
• ⚡ Efficient inference. With DSpark decoding acceleration (DSpark-BF16, DSpark-GGUF), MiniCPM-OCR 1.0 improves end-to-end processing efficiency by 55%. Its throughput is approximately 1.49× that of PaddleOCR-VL-1.6 and 3.62× that of MinerU2.5 Pro, reducing the time needed for large-scale document parsing.
• 🔍 General recognition and information extraction: MiniCPM-OCR 1.0 achieves a SOTA score of 54.7 on OCRBench v2, demonstrating strong general OCR capabilities. It also supports key information extraction (KIE), extracting relevant document content for downstream information management and automated processing.
• 🛠️ Easy deployment and development: MiniCPM-OCR 1.0 supports vLLM, SGLang, llama.cpp, and Ollama from day one, with multiple chip platforms and quantized versions available at release. It supports further fine-tuning and provides a pip SDK and a full-featured API for application integration and customization. You can also get started quickly with SKILL.md. Explore parsing results with the online Demo (CN / Global).

Among the open-source OCR models shown below, MiniCPM-OCR 1.0 achieves 1.2 pages/s and the highest overall performance on OmniDocBench v1.6, Real5-OmniDocBench, Wild-OmniDocBench, and olmOCR-Bench.

_{See the reproduction guide for test settings and reproduction details, and the model guide throughput evaluation for each model's throughput.}

See the MiniCPM-OCR 1.0 guide for per-benchmark bar charts and throughput details.

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

MiniCPM-OCR Jev is an OCR correctness decision model for text, formulas, and tables. Given an image and its corresponding OCR result, it returns a quality score from 0 to 1 to help select reliable results and locate content that needs review.

• 🛡️ Strong performance in a small model: With only 2B parameters, MiniCPM-OCR Jev achieves an AUROC (×100) of 93.6 on JevOCRBench, outperforming JEV-35B-A3B (90.1) and approaching Qwen3.5-397B-A17B (93.8).
• 🧩 Consistent generalization: Across parsing results from MiniCPM-OCR 1.0, GLM-OCR, PaddleOCR-VL-1.6, and Xiaomi-OCR-0, its AUROC (×100) ranges from 91.9 to 93.4.

The table below reports overall and per-category AUROC (×100) on JevOCRBench. Higher values indicate better discrimination between correct and incorrect parsing results.

Accuracy at different review-free rates across three document types.

Set the threshold only after checking it on a small batch from the real application.

Jev scores for text, formula, and table recognition.

See the MiniCPM-OCR Jev model guide for more on usage, technical specifications, evaluation results, and efficiency results.

Choose how to integrate MiniCPM-OCR 1.0 and MiniCPM-OCR Jev:

Ask a coding agent to follow the MiniCPM-OCR 1.0 skill to set up the environment, download models, and parse your first document. Send the following instruction to the agent:

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

Ask a coding agent to follow the MiniCPM-OCR Jev skill to deploy, start, and verify the scoring service locally — either from the Docker image or with the SDK, using vLLM, SGLang, llama.cpp, or Ollama as the inference backend. Send the following instruction to the agent:

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

If you have already cloned this repository, you can use ./docs/SKILL_en.md and ./docs/SKILL_jev_en.md directly.

Use the Free API to access MiniCPM-OCR 1.0 recognition and MiniCPM-OCR Jev quality scoring.

MiniCPM-OCR 1.0 API docs  ·  MiniCPM-OCR Jev API docs

Run the models on your own hardware: MiniCPM-OCR 1.0 parses PDFs and images into Markdown or JSON, while MiniCPM-OCR Jev scores the parsing results.

MiniCPM-OCR 1.0 deployment tutorial · MiniCPM-OCR Jev deployment tutorial · Web demo deployment tutorial

Model	Description	Main Model Weights	DSpark Heads
MiniCPM‑OCR 1.0	Efficient document parsing and text recognition for Chinese and English documents and PDFs, balancing quality and speed across scans, photographs, curved pages, and recaptured documents.	🤗   BF16 🤗   GGUF 🤗   AWQ 🤗   GPTQ	🤗   BF16 🤗   GGUF
MiniCPM‑OCR Jev	An OCR correctness decision model for text, formulas, and tables. Given an image and its OCR result, it returns a quality score from 0 to 1.	🤗   BF16 🤗   GGUF 🤗   AWQ 🤗   GPTQ

See the FAQ for common questions about installation, model downloads, GPU memory, input formats, and unexpected outputs.

The entire project is released under the Apache License 2.0, including code, documentation, and model weights for MiniCPM-OCR 1.0 and MiniCPM-OCR Jev.