抗印章干扰的KCR：一种鲁棒的印章干扰下久世文字符识别框架
A Multi-Stage Framework for Kuzushiji Character Recognition in Japanese Historical Documents
AI总结：
针对印章干扰导致久世文字符识别性能下降的问题，提出一种结合文档修复和合成数据增强的抗印章干扰框架，在真实和合成测试集上分别降低字符错误率39.7%和50.1%。
摘要：
久世文是前现代日本最广泛使用的草书书写系统之一。由于其高度草书的形态和广泛的字形变化，大多数现代日本读者无法阅读久世文字符。因此，近年来的研究集中在开发自动化久世文字符识别（KCR）方法，这些方法在相对干净的日本历史文档图像上取得了强劲性能。尽管印章经常出现在日本历史文档中，现有方法在印章干扰下，特别是当印章与字符重叠时，往往无法保持识别精度。为了应对这一挑战，我们提出了一种抗印章干扰的KCR框架。基于字符检测、分类和排序，所提出的框架额外引入了文档修复以减轻印章干扰，从而提升整体识别性能。此外，我们引入了一种新颖的合成数据增强策略来增强字符检测模型的性能。我们进一步纠正了标注错误，重构了数据集，并创建了一个合成测试集以模拟严重的印章干扰。实验结果表明，所提出的框架在减轻印章干扰对KCR的影响方面是有效的。与常规基线和NDLkotenOCR相比，它在真实测试集上分别实现了39.7%和5.9%的相对字符错误率（CER）降低，在合成测试集上分别实现了50.1%和41.7%的降低。
英文摘要：
Kuzushiji was a widely used cursive writing system in pre-modern Japan. Due to simplification and glyph variation, most modern Japanese readers cannot read Kuzushiji characters. Consequently, recent studies have developed optical character recognition (OCR) systems for Kuzushiji. Despite recent progress, Kuzushiji character recognition (KCR) in Japanese historical documents remains challenging because of seal-character overlap and complex layouts, which interfere with character recognition and hinder accurate reconstruction of the reading order. To address these challenges, we propose a multi-stage KCR framework comprising character detection, cropping, classification, ordering, and large language model (LLM)-based post-OCR correction. Specifically, we employ a synthetic data augmentation strategy to improve character detection robustness against seal interference and introduce an adaptive column clustering algorithm to reconstruct the reading order. Finally, we leverage the contextual capabilities of the LLM to correct OCR errors. In addition, we correct annotation omissions, reconstruct the benchmark dataset, and introduce a synthetic test set with simulated seal interference and an out-of-domain (OOD) test set for evaluation. Compared with the conventional character-level OCR baseline, our framework achieves relative CER reductions of 43.48%, 46.02%, and 39.11% on the real, synthetic, and OOD test sets, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
