Abstract
Kindai documents, written in modern Japanese from the late 19th to early 20th century, hold significant historical value for researchers studying societal structures, daily life, and environmental conditions of that period. However, transcribing these documents remains a labor-intensive and time-consuming task, resulting in limited annotated data for training optical character recognition (OCR) systems. This research addresses this challenge of data scarcity by leveraging parallel textline images - pairs of original Kindai text and their counterparts in contemporary Japanese fonts - to augment training datasets. We introduce a distance-based objective function that minimizes the gap between self-attention features of the parallel image pairs. Specifically, we explore Euclidean distance and Maximum Mean Discrepancy (MMD) as domain adaptation metrics. Experimental results demonstrate that our method reduces the character error rate (CER) by 2.23% and 3.94% over a Transformer-based OCR baseline when using Euclidean distance and MMD, respectively. Furthermore, our approach improves the discriminative quality of self-attention representations, leading to more effective OCR performance for historical documents.
Abstract (translated)
翻译成中文： 近代文献，即19世纪晚期至20世纪初期用现代日语编写的文件，在研究当时的社会结构、日常生活和环境条件方面具有重要的历史价值。然而，将这些文档转录仍然是一个耗时且费力的任务，导致用于训练光学字符识别（OCR）系统的注释数据有限。本研究通过利用平行文本行图像——即原始Kindai文本及其当代日文字体的对应版本——来解决这一数据稀缺问题，以扩充训练数据集。我们引入了一个基于距离的目标函数，该函数可以最小化并行图像对自注意力特征之间的差距。具体来说，我们将欧几里得距离和最大均值差异（MMD）作为领域适应度量进行研究。实验结果表明，与使用欧几里得距离的Transformer基础OCR相比，我们的方法将字符错误率降低了2.23%，而使用MMD则降低了3.94%。此外，我们的方法提高了自注意力表示的判别质量，从而为历史文献提供了更有效的OCR性能。
URL
https://arxiv.org/abs/2508.08537
