SeerRay-Lab / Xiaomi-OCR-0

Xiaomi-OCR-0 
 A unified 0.8B model for document parsing and OCR-related understanding. 
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Xiaomi-OCR-0 is a unified 0.8B vision-language model for document parsing and OCR-centric understanding. Starting from Qwen3.5-0.8B, it is trained on an approximately 170M-sample OCR-centric corpus with Q-Mask text anchoring, continued pretraining (CPT), and mixed-task reinforcement learning (Mix-RL).
## 🏆 Benchmark highlights
- 🥇 **95.24** on Real5-OmniDocBench, ranked **#1** in the comparison on this page.
- 🥈 **96.83** on OmniDocBench v1.6 and **87.94** on Wild-OmniDocBench, both ranked **#2** in the comparisons on this page.
- 📐 **97.84** on UniMER-Test (formula recognition) and **92.27 TEDS** on PubTabNet (table recognition).
- ✍️ Historical handwriting and calligraphy: **73** on Chronicles-OCR Mature Scripts (Clerical, Cursive, Running, and Regular scripts), on par with Seed 2.0 Pro(72).
- 🧠 Mean score of **83.2** across five OCR-centric VQA benchmarks, exceeding Qwen3.5-2B (**80.9**) with **0.8B** parameters.
The document parsing benchmark results above (Real5-OmniDocBench, OmniDocBench v1.6, and Wild-OmniDocBench) all use the two-stage method. See evaluation_pipeline for the evaluation procedure.
see Key Performance for the comparison tables and scope.
## 📰 News
- **2026-10-08:** Thanks to the community for supporting Xiaomi-OCR-0 and sharing new formats: prithivMLmods’ GGUF, xzl01’s GGUF, and ByronLeeee’s BF16 NInfer conversion.
- **2026-09-30:** Technical report published and model weights released.
## What it does
- **Document parsing:** reads document pages and produces structured Markdown, with OTSL model outputs converted to HTML tables in Markdown and formulas in LaTeX.
- **Key information extraction (KIE):** extracts requested fields as JSON.
- **OCR-centric visual question answering (VQA):** answers questions about text and content in document images.
- **Local agent tools:** an MCP service handles prompts, PDF/image preparation, optional region detection, parallel crop inference, and output assembly.
- **Browser demo:** try page and region parsing through a local web interface.
For printed, regular documents, region parsing can detect and process regions concurrently. For scene text, handwriting, calligraphy, historical books, and irregular layouts, use whole-page parsing because incorrect region boundaries can lose context.
## Repository contents
| Path | Contents |
|:--|:--|
| `skills/xiaomi-ocr/` | Agent Skill and MCP service for OCR, PDF parsing, KIE, and VQA |
| `demo/` | Local browser demo and sample cases |
| `example_pics/` | Example inputs, reference Markdown, and demo animations mirrored from the Hugging Face model repository |
| `evaluation_pipeline/` | Reproducible e2e / two-stage inference and evaluation |
| `postprocess/` | Shared table conversion, formula/text assembly and repetition handling |
| Hugging Face model | Model card, examples, and benchmark assets |
| ModelScope model | Model card, examples, and be