针对19世纪保加利亚历史文献，提出首个文本纠错基准与合成数据生成方法。
Post-OCR Text Correction for Bulgarian Historical Documents
- 基于现代文本生成历史拼写的合成数据，解决标注数据稀缺问题。
- 使用大模型与注意力机制，使纠错后文档质量提升25%，优于现有方法16%。
- 适合历史文献数字化、语言技术研究者参考，开源代码与数据可复用。
历史文献数字化对保护文化遗产至关重要。关键步骤是通过光学字符识别（OCR）将扫描图像转为文本，以便后续检索和信息提取。然而，标准OCR工具难以处理历史拼写和复杂版式，因此通常需在OCR输出后进行文本纠错。本文聚焦保加利亚语，首次构建用于评估历史保加利亚文献文本纠错的基准数据集，涵盖19世纪首个标准化拼写——德拉诺夫拼写。同时，利用大量当代保加利亚文学文本，开发了自动生成该拼写及后续伊万切夫拼写合成数据的方法。在此基础上，采用先进的大语言模型与编码器-解码器框架，并引入对角线注意力损失、复制机制与覆盖机制，显著提升纠错效果。所提方法使识别错误减少，文档质量提升25%，在ICDAR 2019保加利亚数据集上较当前最优方法提高16%。相关数据与代码已公开于https://github.com/angelbeshirov/post-ocr-text-correction。
原文摘要 · Abstract (English)
The digitization of historical documents is crucial for preserving the cultural heritage of the society.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{https://github.com/angelbeshirov/post-ocr-text-correction}.}
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