Abstract
Handwriting recognition is a key technology for accessing the content of old manuscripts, helping to preserve cultural heritage. Deep learning shows an impressive performance in solving this task. However, to achieve its full potential, it requires a large amount of labeled data, which is difficult to obtain for ancient languages and scripts. Often, a trade-off has to be made between ground truth quantity and quality, as is the case for the recently introduced Bullinger database. It contains an impressive amount of over a hundred thousand labeled text line images of mostly premodern German and Latin texts that were obtained by automatically aligning existing page-level transcriptions with text line images. However, the alignment process introduces systematic errors, such as wrongly hyphenated words. In this paper, we investigate the impact of such errors on training and evaluation and suggest means to detect and correct typical alignment errors.
Abstract (translated)
手写字符识别是访问旧手稿内容的关键技术，有助于保护文化遗产。深度学习在解决这个任务方面表现出令人印象深刻的性能。然而，要实现其全部潜力，需要大量标记数据，这对古代语言和文字来说很难实现。通常，在真实值数量和质量之间必须做出权衡，正如最近推出的Bullinger数据库所示。它包含超过100,000个标记文本行图像，主要是从中自动对齐现有页面级转录与文本行图像。然而，对齐过程引入了系统误差，例如错拼单词。在本文中，我们研究了这种错误对训练和评估的影响，并提出了一种检测和纠正典型对齐错误的手段。
URL
https://arxiv.org/abs/2312.09037
