Interactive Handwritten Database Query Research

这项研究的目的是支持与包括手写文档的大型数据库的交互。 其目的是提高对交互式查询大型文档数据库的方法的理解。 这些文档的例子包括医疗和保险信息系统，其中大量的纸质记录已经被扫描到其中，政府安全系统和电子历史文档集合，例如由模型版本伙伴关系创建的集合。 一个现有的，高性能的手写识别算法将适应的研究问题。 要解决的问题包括：哪些全球性的，字级的培训目标函数导致更好的性能？ 哪种字符类置信度度量可以获得更好的系统性能？ 以及，哪些基于字符的，字级置信度估计结果 更好的系统性能？ 实验将 重点 比较不同的训练方法;不同的目标函数，优化标准和优化方法;以及估计字符置信度的不同方法。

This research is aimed at supporting interaction with large databases that include handwritten documents. The objective is to advance understanding of methodologies for interactively querying large document databases. Examples of such documents include medical and insurance information systems into which volumes of paper records have been scanned, government security systems, and electronic historical document collections, such as the collections created by the Model Editions Partnership. An existing, high-performance handwritten recognition algorithm will be adapted to the research problem. The questions to be addressed include: Which global, word-level training objective functions result in better performance? Which character class confidence measures result in better system performance? And, which character-based, word level confidence estimators results in better system performance? Experiments will focus on comparison of different methodologies for training; of different objective functions, optimization criteria, and optimization methodologies; and of different methods of estimating character confidence.