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xiaomi ocr 0
Turn scanned pages, tables, formulas and handwriting into clean structured text with Xiaomi OCR 0 — a tiny 0.8B vision-language model you can try free.
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Inside Xiaomi OCR 0: One Engine, Every Page Type
A single 0.8B checkpoint handles page parsing, OCR-driven Q&A and key-field extraction — replacing a whole stack of narrow utilities with one download.
- All-in-One 0.8B CheckpointPage parsing, OCR-focused visual Q&A and field extraction share one lightweight set of weights.
- Benchmark LeaderIt ranks first on Real5-OmniDocBench with 95.24 and takes second on OmniDocBench v1.6 as well as the Wild variant.
- Reliable on Messy ScansWarped, skewed, photographed or photocopied sheets still parse cleanly — not just crisp digital PDFs.
Run Xiaomi OCR 0 in Three Steps
From a raw page image to tidy structured text in three quick moves.
Core Capabilities of Xiaomi OCR 0
Compact size, resilient parsing, handwritten-script support and openly released weights define what this 0.8B model does well.
Page Parsing
Covers ordinary layouts along with scanned, bent, tilted and re-photographed sheets.
Tables and Equations
Equation recognition hits 97.84 on UniMER-Test, and table structure scores 92.27 on PubTabNet.
Handwritten Scripts
Reaches 73 on Chronicles-OCR for mature handwriting, matching Seed 2.0 Pro.
OCR-Focused Visual Q&A
Averages 83.2 over five visual question answering suites, edging past a rival twice its size.
Structured Field Capture
Returns organized fields from receipts and forms rather than flat transcription.
Open Weights, Local Runs
Apache-2.0 checkpoints ship with GGUF and BF16 versions for llama.cpp, Ollama and Transformers.
Xiaomi OCR 0: Questions Answered
Straight answers on what this compact model does, how it scores and where you can run it.
What exactly is this model?
It is a 0.8B vision-language checkpoint tuned for document reading: it parses pages and answers OCR-style questions in a single pass.
Where does it rank on benchmarks?
First on Real5-OmniDocBench, second on OmniDocBench v1.6 and on Wild-OmniDocBench.
Does it handle handwritten text?
Yes — clerical, cursive, running and regular calligraphy all work, scoring 73 on Chronicles-OCR.
What went into its training?
Around 170M OCR-heavy samples, Q-Mask text anchoring, continued pretraining and mixed-task reinforcement learning.
Can it run offline on my machine?
Yes. Apache-2.0 weights are public, and community GGUF and BF16 builds work with llama.cpp, Ollama and Transformers.
Is transcription all it does?
No — it also answers questions about a page and returns key fields as structured output.
Put Xiaomi OCR 0 to Work on Your Files
Upload one page image and get back clean text, tables and structured fields in moments.