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MiniCPM-OCR-1.0, Xiaomi-OCR-0, and Unlimited-OCR-2: three domestic…Developments
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Xiaomi open-sources lightweight document understanding model Xiaomi-OCR-0
Source:IT Home · AI
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Oct 31
Shao Meng
MiniCPM-OCR-1.0, Xiaomi-OCR-0, and Unlimited-OCR-2: three domestic…
The author conducted a side-by-side comparison of the three latest OCR models released by OpenBMB, Xiaomi, and Baidu. MiniCPM-OCR-1.0 (1.3B) has the highest throughput at 1.217 pages/s, with a score of 96.51 on OmniDoc v1.6; Xiaomi-OCR-0 (0.8B) has the fewest parameters, scoring 95.24 on Real5 and averaging 83.2 across the five OCR-VQA leaderboards; Unlimited-OCR-2 (3B/A0.5B) focuses on reading ultra-long documents in a single pass, scoring 77.06 on its self-built MarathonDocBench, but its throughput is only 0.246 pages/s. The author provides model selection recommendations for three types of scenarios: single-page understanding, batch pipelines, and cross-page long documents.
Phoenix Technology
Xiaomi Open-Sources 0.8B Document Understanding Model Xiaomi-OCR-0
Xiaomi has released and open-sourced a lightweight document understanding model, Xiaomi-OCR-0, with only 0.8B parameters, integrating three capabilities—document parsing, key information extraction, and OCR visual question answering—into one. The model scored 95.24 on Real5-OmniDocBench, ranking first among compared models, and scored 96.83 on OmniDocBench v1.6, ranking first among models under 1B parameters, with an average score of 83.2 across five mainstream OCR visual question answering benchmarks. Xiaomi has also open-sourced the model weights and inference code, and provides a Demo, MCP service, and Agent Skill toolkit. The related technology has already been used in Xiaomi phones and "Super Xiao Ai" for screen information recognition such as pickup codes, bills, and cards.
Oct 30
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Xiaomi open-sources the 0.8B-parameter OCR model Xiaomi-OCR-0, supporting Markdown and JSON output
Xiaomi has open-sourced the lightweight document understanding model Xiaomi-OCR-0, with a parameter scale of 0.8B; its weights and inference code are now available on Hugging Face and GitHub. The model is built on Qwen3.5-0.8B-Base and trained on approximately 170 million OCR and document understanding samples. A single model covers three types of tasks: document parsing, key information extraction, and OCR visual question answering, and it natively supports Markdown, LaTeX, and JSON output. Official evaluations show that it scores 96.83 on OmniDocBench v1.6, ranking second on the overall leaderboard and first among models with fewer than 1B parameters. It also provides a browser Demo, an MCP service, and an Agent Skill toolkit.
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