EAGER: Automatic Identification of Writer Accent and Script Influences in Handwriting

EAGER：自动识别手写中的书写者口音和脚本影响

• 批准号: 1014540
• 负责人: Venugopal Govindaraju
• 金额: $ 15万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1014540&HistoricalAwards=false
• 关键词: EAGER; Automatic; Identification; Writer; Accent

This innovative project proposes to explore new hypotheses with respect to analysis of handwriting by examining and analyzing the written works of native and non-native writers of a particular script or alphabet. One hypothesis to be explored is that handwriting can be taken as analogous to speech in the sense that emphases might be indicated in the script itself without special notation. A second hypothesis is that an individual's handwriting is a mixture of style influences that can be decomposed into identifiable constituents. Insight into these issues could result in expansion of our ability to identify important aspects of written works that could benefit research and applications in a variety of disciplines including computing and computational sciences, forensics, biometrics and also the humanities. Humanists would gain powerful new tools and interfaces for analyzing large collections of handwritten documents in many alphabets and scripts and thus be able to ascertain critical information of use in such tasks as chronological ordering, categorization, determination of geographic origins, etc. A number of scripts and alphabets will be included in the research including Arabic, Oriental scripts, Roman and numerous others with significant feature differences.

这个创新的项目提出通过检查和分析特定文字或字母的本地和非本地作家的书面作品，探索关于笔迹分析的新假设。一个需要探索的假设是，手写可以被视为类似于语言，因为重点可以在脚本本身中表示，而不需要特殊的符号。第二个假设是，一个人的笔迹是风格影响的混合体，可以分解成可识别的成分。深入了解这些问题可以扩大我们识别书面作品重要方面的能力，这些方面可以有利于各种学科的研究和应用，包括计算和计算科学、法医学、生物识别学和人文科学。人文学者将获得强大的新工具和界面，用于分析大量以多种字母和文字写成的手写文件，从而能够确定在诸如时间顺序、分类、确定地理来源等任务中使用的关键信息。一些文字和字母将包括在研究中，包括阿拉伯语，东方文字，罗马和许多其他显著特征差异。