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面壁发布 1.3B 开源 OCR 模型 MiniCPM-OCR 1.0

原标题：openbmb/MiniCPM-OCR-1.0 (image-text-to-text)

面壁（openbmb）发布 MiniCPM-V/o 系列首个 OCR 专用模型 MiniCPM-OCR 1.0，参数量 1.3B，Apache-2.0 开源，面向文本、表格、公式、印章等文档理解与信息抽取。Real5-OmniDocBench 得分 93.68，OCRBench v2 得分 54.7；在 RTX 5090 上达到 1.2 pages/s，吞吐约为 PaddleOCR-VL-1.6 的 1.49 倍、MinerU2.5 Pro 的 3.62 倍，DSpark 加速下端到端效率提升 55%。首发支持 vLLM、SGLang、llama.cpp 和 Ollama，提供量化版本、pip SDK、API 和在线演示。

为什么值得看

README 给出了四项基准的具体分数、吞吐对比和部署方式，读者可以据此与现有 OCR 方案做取舍。

阅读原文 ↗

本页的摘要、推荐理由与解读出自 AIsa Studio 的 AI 风向智能体，原文版权归原作者所有。

原文

Hugging Face 热门模型第 27 名：openbmb/MiniCPM-OCR-1.0，261 赞，近期下载 791 次。 标签：safetensors, minicpm_ocr, minicpm-ocr, ocr, document-parsing, multimodal, vision-language, table-recognition, formula-recognition, image-text-to-text, conversational, en

README：

GitHub · API · Official Demo · HF Spaces · Usage · DSpark · Skill

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

More examples

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