TextMonkey:一个没有OCR的大型多式模式,用于理解文档
IEEE transactions on pattern analysis and machine intelligence
|January 26, 2026
概括
一个大型多式模式 (LMM) 的TextMonkey,使用转移窗口注意力和令牌相似性过来增强以文本为中心的任务. 它在场景文本,文档理解和关键信息提取任务方面取得了显著的改进.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 大型多式联运模型 (LMM) 对于复杂的任务越来越重要.
- 现有的LMM在以文本为中心的应用程序中面临挑战,特别是高分辨率输入和令牌效率.
研究的目的:
- 介绍TextMonkey,一个优化用于以文本为中心的任务的LMM.
- 为了提高文档理解和文本识别的性能和可解释性.
主要方法:
- 实现了转移窗口注意力,以增强跨窗口连接和训练稳定性.
- 利用基于相似性的令牌过来减少冗余并提高效率.
- 集成文本发现,接地和位置信息,以提高可解释性.
主要成果:
- 在以场景文本为中心的任务中取得了5.2%的改进,在以文档为中心的任务中取得了6.9%.
- 在场景文本发现方面表现出10.9%的增长,并在OCRBench (561分) 上制定了新的标准.
- 在文档理解基准中表现优于以前的开源LMM.
结论:
- 文本Monkey在以文本为中心的LMM功能方面取得了重大进展.
- 该模型的新技术带来了卓越的性能和可解释性.
- TextMonkey为开源文档理解模型建立了一个新的基准.
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