Bayesian background models for keyword spotting in handwritten documents

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• 作者：Gaurav Kumar ; ^{gauravku@buffalo.edu}Author Vitae ; Venu GovindarajuAuthor Vitae
• 关键词：Handwriting recognition ; Keyword spotting ; Bayesian generalized linear models ; Bayesian generalized kernel models
• 刊名：Pattern Recognition
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：64
• 期：Complete
• 页码：84-91
• 全文大小：934 K
• 卷排序：64

文摘

We propose Bayesian background models for keyword spotting in handwritten documents. We cover a detailed illustration of two of the proposed Bayesian background models. The Bayesian formulation adds uncertainty to handle variation in writing styles. The weights learned on the individual samples provide better rejection criteria. A line level approach that avoids any error is introduced by the word segmentation.