OmniOCR: Generalist OCR for Ethnic Minority Languages
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.
699e984fdfbcf0b800aecbbc
Bonan Liu
699e984fdfbcf0b800aecbbd
Zeyu Zhang
claimed_verified
2026-02-25T17:29:06.050Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
SteveZeyuZhang
user
SteveZeyuZhang
699e984fdfbcf0b800aecbbe
Bingbing Meng
699e984fdfbcf0b800aecbbf
Han Wang
699e984fdfbcf0b800aecbc0
Hanshuo Zhang
699e984fdfbcf0b800aecbc1
Chengping Wang
699e984fdfbcf0b800aecbc2
Daji Ergu
699e984fdfbcf0b800aecbc3
Ying Cai
OmniOCR: Generalist OCR for Ethnic Minority Languages
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.
OmniOCR presents a universal framework for ethnic minority scripts using Dynamic LoRA and sparsity regularization to achieve state-of-the-art accuracy with improved parameter efficiency in low-resource settings.
Dynamic Low-Rank Adaptation Dynamic LoRA sparsity regularization zero-shot foundation models post training parameter efficiency