Abstract
UNESCO has classified 2500 out of 7000 languages spoken worldwide as endangered. Attrition of a language leads to loss of traditional wisdom, folk literature, and the essence of the community that uses it. It is therefore imperative to bring digital inclusion to these languages and avoid its extinction. Low resource languages are at a greater risk of extinction. Lack of unsupervised Optical Character Recognition(OCR) methodologies for low resource languages is one of the reasons impeding their digital inclusion. We propose VOLTAGE - a contrastive learning based OCR methodology, leveraging auto-glyph feature recommendation for cluster-based labelling. We augment the labelled data for diversity and volume using image transformations and Generative Adversarial Networks. Voltage has been designed using Takri - a family of scripts used in 16th to 20th century in the Himalayan regions of India. We present results for Takri along with other Indic scripts (both low and high resource) to substantiate the universal behavior of the methodology. An accuracy of 95% for machine printed and 87% for handwritten samples on Takri script has been achieved. We conduct baseline and ablation studies along with building downstream use cases for Takri, demonstrating the usefulness of our work.
Abstract (translated)
联合国教科文组织将全球约7000种语言中的2500种列为濒危语言。一种语言的衰落会导致传统智慧、民间文学以及使用该语言的社区本质的丧失。因此，有必要为这些语言带来数字包容性，以避免其灭绝。资源较少的语言面临更高的灭绝风险。缺乏针对低资源语言的无监督光学字符识别（OCR）方法是阻碍它们实现数字包容性的原因之一。我们提出了一种基于对比学习的方法——VOLTAGE，该方法利用自动字形特征推荐进行基于聚类的标签生成，并通过图像变换和生成对抗网络来扩充标注数据集的多样性和规模。VOLTAGE使用印度喜马拉雅地区16至20世纪使用的Takri文字家族设计开发而成。我们提供了Takri以及其他印地语（包括资源较少和较多的语言）的实验结果，以证明该方法具有普遍适用性。在Takri字符上，机器打印样本和手写样本分别达到了95%和87%的准确率。我们还进行了基线测试、消融研究，并构建了Takri语言下游应用场景，以展示我们的工作的实用性。
URL
https://arxiv.org/abs/2510.10490
