In recent years, research on visual document understanding (VDU) has grown significantly, with a particular emphasis on the development of self-supervised learning methods. However, one of the significant challenges faced in this field is the limited availability of publicly accessible visual corpora or extensive collections of images with detailed text annotations, particularly for non-Latin or resource-scarce languages. To address this challenge, we propose Web-based Visual Corpus Builder (Webvicob), a dataset generator engine capable of constructing large-scale, multilingual visual corpora from raw Wikipedia HTML dumps. Our experiments demonstrate that the data generated by Webvicob can be used to train robust VDU models that perform well on various downstream tasks, such as DocVQA and post-OCR parsing. Furthermore, when using a dataset of 1 million images generated by Webvicob, we observed an improvement of over 13% on the DocVQA Task 3 compared to a dataset of 11 million images from the IIT-CDIP. The implementation of our engine is publicly available on https://github.com/clovaai/webvicob

翻译：近年来，视觉文档理解（VDU）领域的研究取得了显著进展，尤其侧重于自监督学习方法的发展。然而，该领域面临的一项重大挑战是：公开可访问的视觉语料库或带有详细文本注释的大规模图像集合十分有限，尤其对于非拉丁文字或资源稀缺语言而言。为解决这一难题，本文提出了基于网络的视觉语料库构建工具（Webvicob）——一种能从Wikipedia原始HTML转储中构建大规模多语言视觉语料库的数据集生成引擎。实验表明，Webvicob生成的数据可用于训练鲁棒的VDU模型，这些模型在DocVQA和OCR后解析等多项下游任务中均表现优异。此外，当使用Webvicob生成的100万张图像数据集时，与IIT-CDIP中1100万张图像的数据集相比，我们在DocVQA任务3上的性能提升超过13%。我们的引擎实现已开源，可访问https://github.com/clovaai/webvicob。