表格式上下文感知光学字符识别和表格式数据重建用于历史记录
Loitongbam Gyanendro Singh1, Stuart E Middleton1
1School of Electronics and Computer Science, University of Southampton, Southampton, UK.
概括
本研究引入了数字化历史表的新框架,包括一个大型数据集和一个先进的OCR管道. 该系统显著提高了复杂和退化文档的准确性.
科学领域:
- 计算机科学 计算机科学
- 数字人文学科 数字人文学科
- 信息科学 信息科学 信息科学
背景情况:
- 数字化历史表格记录对于数据的保存和分析至关重要.
- 挑战包括复杂的布局,混合文本类型和文档质量下降.
- 现有的方法与历史文件的复杂性作斗争.
研究的目的:
- 为强大的历史表数字化提供一个全面的框架.
- 引入一个新的数据集和情境感知文本提取方法.
- 开发一个完善的端到端OCR管道,以提高准确性.
主要方法:
- 开发了UoS_Data_Rescue,一个包含1113个历史日志的数据集,包含超过594,000个注释文本单元.
- 提出了TROCR-ctx,这是一个情境感知文本提取方法,以尽量减少级联错误.
- 集成的TROCR-ctx与Byt5在一个统一的OCR和后OCR校正框架.
主要成果:
- 实现了0.049的单词错误率和0.035.5的字符错误率.
- 在OCR任务中,超过现有方法的性能高达41%.
- 在表重建任务中表现出10.74%的改进.
结论:
- 拟议的框架为复杂的表格文件的大规模数字化提供了一个强大的解决方案.
- 该系统提高了多语言和退化文本的识别精度.
- 数据集和实现可以作为开源资源用于更广泛的应用.
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