[Submitted on 28 Sep 2026]
Title:Xiaomi-OCR-0 Technical Report
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Abstract:Compact OCR-specific vision-language models achieve strong document parsing performance, but often rely on costly supervision and focus primarily on visual-text reconstruction. We introduce Xiaomi-OCR-0, a unified 0.8B model for document parsing and OCR-centric understanding. We build an approximately 170M-sample OCR-centric corpus using an automated data engine that combines expert consensus, render-based verification, and targeted synthesis. Starting from Qwen3.5-0.8B, our progressive training recipe combines Q-Mask-based text anchoring, continued pretraining, and mixed-task reinforcement learning (Mix-RL). Xiaomi-OCR-0 achieves 95.24 on Real5-OmniDocBench, 96.83 on OmniDocBench v1.6, and 87.94 on Wild-OmniDocBench, while reaching an average score of 83.2 across five OCR-oriented VQA benchmarks. Ablations further show that, with sufficient parsing training, OCR-centric understanding supervision provides additional gains for document parsing.
Homepage: this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.36136 [cs.CV]
(or arXiv:2609.36136v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.36136
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Longwei Xu [view email] [v1] Mon, 28 Sep 2026 19:10:10 UTC (5,485 KB)
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