Generalized hidden Markov models. II. Application to handwritten word recognition
Generalized hidden Markov models. II. Application to handwritten word recognition
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DOI：
10.1109/91.824774
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发表时间：
2000-02
期刊：
影响因子：
--
通讯作者：
M. Mohamed;P. Gader
中科院分区：
文献类型：
--
作者：
M. Mohamed;P. Gader
关于这一部分，见同上，第8卷，第1期（2000年）。本文提出了广义隐马尔可夫模型在手写体识别中的应用。该系统通过对从给定单词图像中的每列计算的特征进行编码，将单词图像表示为观察向量的有序列表。单词模型是通过连接组成字符隐马尔可夫模型的状态链而形成的。新的工作提出了包括预处理，特征提取，广义隐马尔可夫模型的手写体字识别的应用。训练的经典和广义（模糊）模型的方法。实验进行了一个标准的数据集上的手写体字的图像从美国邮政局的邮件流，其中包含不同的风格和质量的真实字样本。
For part I see ibid. vol.8, no. 1 (2000). This paper presents an application of the generalized hidden Markov models to handwritten word recognition. The system represents a word image as an ordered list of observation vectors by encoding features computed from each column in the given word image. Word models are formed by concatenating the state chains of the constituent character hidden Markov models. The novel work presented includes the preprocessing, feature extraction, and the application of the generalized hidden Markov models to handwritten word recognition. Methods for training the classical and generalized (fuzzy) models are described. Experiments were performed on a standard data set of handwritten word images obtained from the US Post Office mail stream, which contains real-word samples of different styles and qualities.