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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Recognizing on-line handwritten alphanumeric characters through flexible structural matching
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Recognizing on-line handwritten alphanumeric characters through flexible structural matching

机译:通过灵活的结构匹配识别在线手写字母数字字符

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摘要

Speed, accuracy, and flexibility are crucial to the practical use of on-line handwriting recognition. Besides, extensibility is also an important concern as we move from one domain to another which requires the character set to be extended. In this paper, we will propose a simple yet robust structural approach for recognizing on-line handwriting. Our approach is designed to achieve reasonable speed, fairly high accuracy and sufficient tolerance to variations. At the same time, it maintains a high degree of reusability and hence facilitates extensibility. Experimental results show that the recognition rates are 98.60% for digits, 98.49% for uppercase letters, 97.44% for lower case letters, and 97.40% for the combined set. When the rejected cases are excluded from the calculation, the rates can be increased to 99.93%, 99.53%, 98.55% and 98.07%, respectively. On the average, the recognition speed is about 7.5 characters per second running in Prolog on a Sun SPARC 10 Unix workstation and the memory requirement is reasonably low. With this simple yet robust structural approach, we already have an effective and efficient on-line character recognition module. This module will be used as part of a larger system, a pen-based mathematical equation editor, which is being developed by the authors using a syntactical pattern recognition approach.
机译:速度,准确性和灵活性对于实际使用在线手写识别至关重要。此外,在我们从一个域转移到另一个域时,可扩展性也是一个重要的问题,这需要扩展字符集。在本文中,我们将提出一种简单而健壮的结构方法来识别在线手写。我们的方法旨在实现合理的速度,相当高的准确性以及对变化的足够容忍度。同时,它保持高度的可重用性,因此有利于扩展。实验结果表明,数字的识别率为98.60%,大写字母为98.49%,小写字母为97.44%,组合集为97.40%。如果排除拒绝案例,则比率可以分别提高到99.93%,99.53%,98.55%和98.07%。平均而言,在Sun SPARC 10 Unix工作站上的Prolog中运行时,识别速度约为每秒7.5个字符,并且内存需求相当低。通过这种简单而强大的结构方法,我们已经有了一个有效且高效的在线字符识别模块。该模块将用作较大系统的一部分,该系统是基于笔的数学方程式编辑器,作者正在使用一种句法模式识别方法来开发该模块。

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