Abstract
The digitization of historical documents is crucial for preserving the cultural heritage of the society. An important step in this process is converting scanned images to text using Optical Character Recognition (OCR), which can enable further search, information extraction, etc. Unfortunately, this is a hard problem as standard OCR tools are not tailored to deal with historical orthography as well as with challenging layouts. Thus, it is standard to apply an additional text correction step on the OCR output when dealing with such documents. In this work, we focus on Bulgarian, and we create the first benchmark dataset for evaluating the OCR text correction for historical Bulgarian documents written in the first standardized Bulgarian orthography: the Drinov orthography from the 19th century. We further develop a method for automatically generating synthetic data in this orthography, as well as in the subsequent Ivanchev orthography, by leveraging vast amounts of contemporary literature Bulgarian texts. We then use state-of-the-art LLMs and encoder-decoder framework which we augment with diagonal attention loss and copy and coverage mechanisms to improve the post-OCR text correction. The proposed method reduces the errors introduced during recognition and improves the quality of the documents by 25\%, which is an increase of 16\% compared to the state-of-the-art on the ICDAR 2019 Bulgarian dataset. We release our data and code at \url{this https URL}.}
Abstract (translated)
历史文献的数字化对于保留社会文化遗产至关重要。这个过程的一个重要步骤是将扫描图像使用光学字符识别（OCR）转换为文本，这将允许进行进一步的搜索、信息提取等。然而，这是一个困难的问题，因为标准的OCR工具并不专门为处理历史拼写以及具有挑战性的布局而设计。因此，在处理这类文档时，通常会在OCR输出上应用一个额外的文本修正步骤。在这项工作中，我们专注于保加利亚语，并创建了评估OCR对第一标准化保加利亚语历史文献的文本修正的第一个基准数据集：19世纪Drinov拼写的文献。我们进一步开发了一种在這種拼写以及随后的Ivanchev拼写中自动生成合成数据的方法，通过利用大量当代保加利亚语文本。然后，我们使用最先进的LLMs和编码器-解码器框架，通过增加梯度注意损失和覆盖机制来增强后OCR文本修正。所提出的方法减少了识别过程中引入的错误，并将文档的质量提高了25\%，与ICDAR 2019年保加利亚数据集中的最先进方法相比，提高了16\%。我们还将数据和代码发布在\url{这个链接}上。
URL
https://arxiv.org/abs/2409.00527
