RI: Small: Coordinating Language Modeling, Computer Vision, and Machine Learning for Dramatic Advances in Optical Character Recognition

RI：小型：协调语言建模、计算机视觉和机器学习，实现光学字符识别的巨大进步

• 批准号: 0916555
• 负责人: Erik Learned-Miller
• 金额: $ 46.34万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916555&HistoricalAwards=false
• 关键词: RI; Small; Coordinating; Language; Modeling

The goal of this research is to develop new methods for improving the performance of optical character recognition (OCR) systems. In particular, the PI investigates "iterative contextual modeling", an approach to OCR in which high confidence recognitions of easier document portions are used to help in developing document specific models. These models can be related to appearance--for example a sample of correct words can be used to develop a model for the font in a particular document. In addition, the models can be based on language and vocabulary information. For example, after recognizing a portion of the words in a document, the general topic of the document may be detected, at which point the distribution over likely words in the document can be changed. The ability to modify character appearance distributions and language statistics and tune them specifically to the document at hand is expected to produce significant increases in the quality of OCR results.

本研究的目标是开发新的方法，以提高光学字符识别（OCR）系统的性能。特别是，PI调查“迭代上下文建模”，一种OCR方法，其中使用更容易的文档部分的高置信度解释来帮助开发文档特定模型。这些模型可以与外观相关--例如，可以使用正确单词的样本来为特定文档中的字体开发模型。此外，模型可以基于语言和词汇信息。例如，在识别文档中的词的一部分之后，可以检测文档的一般主题，此时可以改变文档中的可能词上的分布。能够修改字符外观分布和语言统计数据，并将它们专门调整到手头的文档，预计将显著提高OCR结果的质量。