Real-time on-line unconstrained handwriting recognition using statistical methods
Real-time on-line unconstrained handwriting recognition using statistical methods
复制标题
使用统计方法实时在线无约束手写识别
DOI：
10.1109/icassp.1995.480098
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
H. Maruyama
中科院分区：
文献类型：
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作者：
Krishna S. Nathan;H. Beigi;J. Subrahmonia;G. J. Clary;H. Maruyama
我们解决了无约束手写文本的自动识别问题。统计方法，如隐马尔可夫模型（hmm）已经成功地应用于语音识别，它们也被应用于手写识别问题。讨论了一种针对大词汇量、写作者独立、不受约束的手写文本的通用识别系统。“不受约束”意味着用户可以用任何风格书写，例如印刷体、草书或任何风格的组合。这是典型的手写文本的代表，很少遇到纯印刷或纯草书形式。此外，该系统的一个关键特征是它可以在486类PC平台上实时执行识别，而不需要传统基于HMM的系统所需的大量内存。我们主要关注作者的独立任务。还报告了一些与初始写入器相关的结果。在没有任何语言模型的情况下，对于独立于作者的21,000单词词汇任务，错误率达到18.9%。
We address the problem of automatic recognition of unconstrained handwritten text. Statistical methods, such as hidden Markov models (HMMs) have been used successfully for speech recognition and they have been applied to the problem of handwriting recognition as well. We discuss a general recognition system for large vocabulary, writer independent, unconstrained handwritten text. "Unconstrained" implies that the user may write in any style e.g. printed, cursive or in any combination of styles. This is more representative of typical handwritten text where one seldom encounters purely printed or purely cursive forms. Furthermore, a key characteristic of the system is that it performs recognition in real-time on 486 class PC platforms without the large amounts of memory required for traditional HMM based systems. We focus mainly on the writer independent task. Some initial writer dependent results are also reported. An error rate of 18.9% is achieved for a writer-independent 21,000 word vocabulary task in the absence of any language models.