全能OCR：少数民族语言通用光学字符识别系统

摘要

随着深度学习和多模态模型的快速发展，光学字符识别（OCR）技术取得了显著进步，但现有方法大多聚焦于拉丁文、中文等资源丰富语种。少数民族文字因书写系统复杂、标注资源稀缺、古今形态多样等因素，在低资源或零样本场景下的泛化能力面临挑战。为此，我们提出通用少数民族文字识别框架OmniOCR。该框架引入动态低秩适配机制（Dynamic LoRA），通过跨层级和跨文字的动态容量分配，在保持原有知识的前提下实现高效适配；同时采用稀疏正则化修剪冗余参数更新，确保无需额外推理成本的紧凑高效适配。在藏文TibetanMNIST、水书、古彝文和东巴文数据集上的实验表明，OmniOCR在零样本基础模型和常规后训练方法中均取得最优效果，以卓越的参数效率达到当前最高识别精度，相较基线模型在这四个数据集上的准确率提升39%-66%。代码地址：https://github.com/AIGeeksGroup/OmniOCR。

English

Optical character recognition (OCR) has advanced rapidly with deep learning and multimodal models, yet most methods focus on well-resourced scripts such as Latin and Chinese. Ethnic minority languages remain underexplored due to complex writing systems, scarce annotations, and diverse historical and modern forms, making generalization in low-resource or zero-shot settings challenging. To address these challenges, we present OmniOCR, a universal framework for ethnic minority scripts. OmniOCR introduces Dynamic Low-Rank Adaptation (Dynamic LoRA) to allocate model capacity across layers and scripts, enabling effective adaptation while preserving knowledge.A sparsity regularization prunes redundant updates, ensuring compact and efficient adaptation without extra inference cost. Evaluations on TibetanMNIST, Shui, ancient Yi, and Dongba show that OmniOCR outperforms zero-shot foundation models and standard post training, achieving state-of-the-art accuracy with superior parameter efficiency, and compared with the state-of-the-art baseline models, it improves accuracy by 39%-66% on these four datasets. Code: https://github.com/AIGeeksGroup/OmniOCR.

全能OCR：少数民族语言通用光学字符识别系统

OmniOCR: Generalist OCR for Ethnic Minority Languages

摘要

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