Abstract
Optical Character Recognition (OCR) for low-resource languages remains a significant challenge due to the scarcity of large-scale annotated training datasets. Languages such as Kashmiri, with approximately 7 million speakers and a complex Perso-Arabic script featuring unique diacritical marks, currently lack support in major OCR systems including Tesseract, TrOCR, and PaddleOCR. Manual dataset creation for such languages is prohibitively expensive, time-consuming, and error-prone, often requiring word by word transcription of printed or handwritten text. We present SynthOCR-Gen, an open-source synthetic OCR dataset generator specifically designed for low-resource languages. Our tool addresses the fundamental bottleneck in OCR development by transforming digital Unicode text corpora into ready-to-use training datasets. The system implements a comprehensive pipeline encompassing text segmentation (character, word, n-gram, sentence, and line levels), Unicode normalization with script purity enforcement, multi-font rendering with configurable distribution, and 25+ data augmentation techniques simulating real-world document degradations including rotation, blur, noise, and scanner artifacts. We demonstrate the efficacy of our approach by generating a 600,000-sample word-segmented Kashmiri OCR dataset, which we release publicly on HuggingFace. This work provides a practical pathway for bringing low-resource languages into the era of vision-language AI models, and the tool is openly available for researchers and practitioners working with underserved writing systems worldwide.
Abstract (translated)
对于资源匮乏的语言，光学字符识别（OCR）仍然是一项重大挑战，主要是因为缺乏大规模的标注训练数据集。像克什米尔语这样的语言，拥有大约700万使用者和复杂的波斯-阿拉伯书写系统，其中包括独特的标点符号，在Tesseract、TrOCR 和 PaddleOCR 等主要 OCR 系统中目前仍得不到支持。为这类语言创建手动数据集的成本高得令人难以承受，耗时且容易出错，并常常需要逐词转录印刷或手写文本。 我们提出了一种开源的合成 OCR 数据集生成器——SynthOCR-Gen，专门针对资源匮乏的语言设计。该工具通过将数字 Unicode 文本语料库转换为即用型训练数据集来解决 OCR 开发中的基本瓶颈问题。系统实现了一个全面的工作流程，包括文本分割（字符、单词、n-gram、句子和行级别）、Unicode 正规化以及强制实施书写系统的纯度，多字体渲染和支持配置的分布设置，以及 25 多种数据增强技术来模拟现实世界文档退化的多种情况，如旋转、模糊、噪声和扫描器产生的伪影。 我们通过生成一个包含60万样本的克什米尔语单词级 OCR 数据集来展示了这种方法的有效性，并将其公开发布在 HuggingFace 上。本工作为资源匮乏的语言进入视觉-语言 AI 模型时代提供了一条实用路径，且工具对全世界从事未得到充分服务的文字系统的研究人员和实践者完全开放使用。
URL
https://arxiv.org/abs/2601.16113
