Use of Language Models in Handwritten Sentence/Phrase Recognition

Srihari 9315006 这是NSF/ARPA人类语言技术的标准奖项，用于合作研究如何利用新的和改进的方法来识别手写文本，并支持基于语言知识的语言模型，涉及布法罗的纽约州立大学和伊士曼柯达公司的研究人员。 该方法涉及使用上下文约束来提高识别，有时，覆盖经典模式识别的结果。 这包括使用词典和整体过滤，词典统计，以及高级语法和语义后处理。 虽然研究的重点是英语语言的模型，重点是语言成分，而不是模式识别成分，并利用统计训练与代表性语料库，它可以扩展到其他欧洲语言和印刷文本的识别。

Srihari 9315006 This is a standard award in the NSF/ARPA Human Language Technology for cooperative research on how to utilize new and improved approaches to the recognition of handwritten text with the support of language models based on linguistic knowledge, involving researchers at the State University of New York at Buffalo and the Eastman Kodak Company. The approach concerns the use of contextual constraints to improve the recognition and, at times, override the results of classical pattern recognition. This includes the use of lexicons and holistic filtering, of dictionary statistics, and of high-level syntactic and semantic post-processing. Although the research focuses on models of the English language, with emphasis on the linguistic component rather than on the pattern recognition component, and utilizes statistical training with representative corpora, it can be extended to other European languages and to the recognition of printed text.