公开(公告)号：US5644652A

公开(公告)日：1997-07-01

申请号：US424236

申请日：1995-04-19

申请人： Eveline Jeannine Bellegarda ,  Jerome Rene Bellegarda ,  David Nahamoo ,  Krishna Sundaram Nathan

发明人： Eveline Jeannine Bellegarda ,  Jerome Rene Bellegarda ,  David Nahamoo ,  Krishna Sundaram Nathan

IPC分类号： G06K9/70 ,  G06K9/62 ,  G06K9/18

CPC分类号： G06K9/6297

摘要： An automatic handwriting recognition system wherein each written (chirographic) manifestation of each character is represented by a statistical model (called a hidden Markov model). The system implements a method which entails sampling a pool of independent writers and deriving a hidden Markov model for each particular character (allograph) which is independent of a particular writer. The HMMs are used to derive a chirographic label alphabet which is independent of each writer. This is accomplished during what is described as the training phase of the system. The alphabet is constructed using supervised techniques. That is, the alphabet is constructed using information learned in the training phase to adjust the result according to a statistical algorithm (such as a Viterbi alignment) to arrive at a cost efficient recognition tool. Once such an alphabet is constructed a new set of HMMs can be defined which more accurately reflects parameter typing across writers. The system recognizes handwriting by applying an efficient hierarchical decoding strategy which employs a fast match and a detailed match function, thereby making the recognition cost effective.

摘要翻译： 一种自动手写识别系统，其中每个字符的每个书写（手绘）表现由统计模型（称为隐马尔可夫模型）表示。 该系统实现了一种方法，该方法需要对独立作者的池进行抽样，并为独立于特定作者的每个特定字符（同位素）导出隐马尔科夫模型。 HMM用于导出独立于每个作者的手写标签字母表。 这是在系统的训练阶段描述的过程中完成的。 字母表使用监督技术构建。 也就是说，使用在训练阶段学习的信息来构建字母表，以根据统计算法（例如维特比对齐）来调整结果，以得到成本有效的识别工具。 一旦构建了这样一个字母表，就可以定义一组新的HMM，它可以更准确地反映作者的参数分类。 该系统通过应用采用快速匹配和详细匹配功能的有效分层解码策略来识别手写，从而使识别成本有效。