Xiaomi has announced the release of the Xiaomi-OCR-0 model, a lightweight document understanding model. The weights and inference code for this model are available on Hugging Face and GitHub, the company reported on October 30th.
The model boasts 0.8 billion parameters and is designed to perform three types of tasks within a single system: document parsing, key information extraction, and visual question answering via OCR.
The parsing functionality includes recognizing main text, tables, and mathematical formulas, outputting structured results in reading order with support for native Markdown and LaTeX formats. For key information extraction, the model focuses on invoices, receipts, IDs, and business forms, locating requested fields and providing the result in JSON format for automated workflows.
Visual question answering goes beyond simple transcription, enabling cross-regional searching, numerical calculations, and reasoning based on documents and charts.
Training and Methodology
Xiaomi states that the model is built upon the Qwen3.5-0.8B-Base model and was trained on approximately 170 million OCR and document samples. Data labels were filtered by achieving consensus among several expert OCR models that performed mutual verification and voted on areas of text, tables, and formulas.
When working with complex tables and formulas, the team utilized a 'predict, re-render, and compare' loop: candidate results were re-rendered into images and compared with the original at the pixel and structural level to detect shifted rows or missing characters. Synthetic complex cases included multilingual text, historical handwriting, screen moiré effects, and distorted lighting.
The training process occurred in three stages: first, textual fine-tuning to align visual regions with text tokens; second, continued pre-training of OCR on tens of billions of tokens; and finally, reinforcement learning for mixed tasks with verifiable rewards, such as edit distance and table matching scores.
Testing Results
On the OmniDocBench v1.6 benchmark, the model achieved a score of 96.83, ranking second overall, only 0.04 points behind the leader, and becoming the top performer among models with less than one billion parameters. In the Real5-OmniDocBench test, which simulates scanning, crumpled paper, screenshots, lighting variations, and tilted frames, the model scored 95.24. Its performance on Wild-OmniDocBench was 87.94.
The model reaches 97.84 on the UniMER-Test reference formula test and 92.27 on PubTabNet for table structure evaluation. Averaging results across DocVQA, InfoVQA, ChartQA, OCRBench, and TextVQA, it scores 83.2, surpassing HunyuanOCR-1.5 with 76.5 and 8B MiniCPM-V-4.5 with 82.6, although it trails Qwen3.5-4B with a score of 84.9. It should be noted that all presented figures are proprietary data from Xiaomi.
Xiaomi reports that this technology is already integrated into the Super XiaoAi assistant on its phones, where it can extract boarding passes, queue numbers, bill details, and meeting times directly from the screen. In addition to the model weights, Xiaomi provides developers with a browser demo version, the MCP service, and an Agent Skill toolkit.
