Interactive Handwritten Database Query Research

这项研究旨在支持与包括手写文档在内的大型数据库的交互。其目的是增进对交互查询大型文件数据库的方法的理解。这类文件的例子包括医疗和保险信息系统、政府安全系统和电子历史文件收藏，如示范版伙伴关系创建的收藏。现有的高性能手写识别算法将适用于这一研究问题。需要解决的问题包括：哪些全球、词汇级的培训目标职能能带来更好的绩效？哪种字符类置信度度量会产生更好的系统性能？那么，哪种基于字符、单词级别的置信度估计器会带来更好的系统性能呢？实验将侧重于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；以及不同的估计字符置信度的方法。

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information systems into which volumes of paper records have been scanned, government security systems, and electronic historical document collections, such as the collections created by the Model Editions Partnership.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.