Abstract
This technical report presents the 600K-KS-OCR Dataset, a large-scale synthetic corpus comprising approximately 602,000 word-level segmented images designed for training and evaluating optical character recognition systems targeting Kashmiri script. The dataset addresses a critical resource gap for Kashmiri, an endangered Dardic language utilizing a modified Perso-Arabic writing system spoken by approximately seven million people. Each image is rendered at 256x64 pixels with corresponding ground-truth transcriptions provided in multiple formats compatible with CRNN, TrOCR, and generalpurpose machine learning pipelines. The generation methodology incorporates three traditional Kashmiri typefaces, comprehensive data augmentation simulating real-world document degradation, and diverse background textures to enhance model robustness. The dataset is distributed across ten partitioned archives totaling approximately 10.6 GB and is released under the CC-BY-4.0 license to facilitate research in low-resource language optical character recognition.
Abstract (translated)
这份技术报告介绍了600K-KS-OCR数据集，这是一个大型合成语料库，包含大约602,000张单字符级别的分割图像，旨在用于训练和评估针对克什米尔语的光学字符识别系统。该数据集解决了克什米尔语资源匮乏的问题，这是一种濒危的达迪语族语言，使用修改后的波斯-阿拉伯书写系统，由约七百万人使用。每一张图像是以256x64像素生成，并附有与CRNN、TrOCR和通用机器学习管道兼容的多个格式的真实文本转录。生成方法采用了三种传统的克什米尔字体，全面的数据增强技术模拟了真实文档退化的状况，并加入了多种背景纹理来提升模型的鲁棒性。该数据集分布在十个分区档案中，总大小约为10.6GB，并在CC-BY-4.0许可下发布以促进低资源语言光学字符识别领域的研究工作。
URL
https://arxiv.org/abs/2601.01088
