Interactive Handwritten Database Query Research

交互式手写数据库查询研究

• 批准号: 9732914
• 负责人: Paul Gader
• 金额: $ 15.55万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-01 至 2001-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9732914&HistoricalAwards=false
• 关键词: Interactive; Handwritten; Database; Query; Research

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information systems into which volumes of paper records have been scanned, government security systems, and electronic historical document collections, such as the collections created by the Model Editions Partnership.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

这项研究旨在支持与包括手写文档在内的大型数据库的互动。 目的是促进对互动查询大型文档数据库的方法论的理解。 此类文件的示例包括医疗和保险信息系统，其中扫描了纸质记录的数量，政府安全系统和电子历史文档收集，例如模型版伙伴关系创建的收集。 现有的高性能手写识别算法将适应研究问题。 要解决的问题包括：哪个全球，单词级培训目标功能可以提高性能？ 哪种角色类置信度衡量可以提高系统性能？ 而且，哪个基于字符的单词置信度估计器会导致更好的系统性能？ 实验将集中于对培训不同方法的比较；不同的目标功能，优化标准和优化方法； 以及估计字符置信度的不同方法。