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xiaomi ocr 0
Xiaomi OCR 0 is a compact 0.8B vision-language model that turns dense pages, tables, formulas and handwriting into clean, structured text.
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Why Use Xiaomi OCR 0
One 0.8B checkpoint covers document parsing, OCR-centric question answering and key information extraction, so a single model replaces a stack of narrow tools.
- Unified 0.8B ModelParsing, OCR-oriented VQA and field extraction live in one compact checkpoint.
- Top-Ranked AccuracyFirst place on Real5-OmniDocBench at 95.24, plus runner-up spots on OmniDocBench v1.6 and its Wild counterpart.
- Steady on Real ScansHolds up on scanned, warped, skewed and photographed pages, not only clean PDFs.
How to Use Xiaomi OCR 0
Three steps take a page image to clean, structured output.
Xiaomi OCR 0 Features
A small footprint, robust parsing, handwriting support and open weights — the practical strengths of this 0.8B OCR model.
Document Parsing
Handles standard pages plus scanned, warped, skewed and recaptured documents.
Tables & Formulas
Its formula score reaches 97.84 on UniMER-Test, while table structure lands at 92.27 on PubTabNet.
Handwriting & Calligraphy
Scores 73 on Chronicles-OCR mature scripts, level with Seed 2.0 Pro.
OCR-Centric VQA
Across five visual question answering suites it averages 83.2, edging past a 2B rival while using a fraction of the size.
Field Extraction
Pulls structured fields from receipts and forms instead of plain transcription.
Open & Local-Ready
Apache-2.0 weights plus GGUF and BF16 builds for llama.cpp, Ollama and Transformers.
Xiaomi OCR 0 — FAQ
Common questions about this compact OCR model, its benchmarks and local deployment.
What is this model?
A 0.8B vision-language checkpoint built specifically for OCR work — it reads documents and handles OCR-centric questions in one pass.
Which benchmarks does it top?
It takes first place on Real5-OmniDocBench and second place on both OmniDocBench v1.6 and Wild-OmniDocBench.
Can it read handwriting?
Yes. Clerical, cursive, running and regular calligraphic scripts are supported, with a score of 73 on Chronicles-OCR.
How was it trained?
Its recipe pairs roughly 170M OCR-focused samples with Q-Mask text anchoring, continued pretraining, and mixed-task reinforcement learning.
Can I run it locally?
Apache-2.0 weights are released, with community GGUF and BF16 conversions for llama.cpp, Ollama and Transformers.
Does it only transcribe text?
No. It also answers questions about a page and extracts key fields into structured output.
Try Xiaomi OCR 0 on Your Own Documents
Drop in a page image and let Xiaomi OCR 0 return parsed text, tables and structured fields in seconds.