Active Handwritten Character Recognition Using Genetic Programming
Active Handwritten Character Recognition Using Genetic Programming
复制标题
使用遗传编程的主动手写字符识别
DOI：
10.1007/3-540-45355-5_30
复制
发表时间：
2001
期刊：
影响因子：
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通讯作者：
V. Govindaraju
中科院分区：
文献类型：
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作者：
A. Teredesai;Jaehwa Park;V. Govindaraju
本文的目的是证明有效地使用遗传规划的手写字符识别。当分类器所使用的资源逐渐增加并依赖于分类任务的复杂度时，我们称这样的分类器为主动分类器。基于遗传规划原理的主动分类器的设计和实现变得非常简单和有效。遗传编程在特征集选择方面对手写体字符识别问题进行了优化。我们提出了一个实现与动态的预处理和分类的手写数字图像。这种模式将补充现有的方法，提供更好的性能方面的准确性和处理时间每图像分类。不同层次的信息细节可以存在于图像数据中，我们提出的范例有助于突出这些信息丰富的区域。我们比较我们的性能与被动和主动的手写数字分类方案，是基于其他模式识别技术。
This paper is intended to demonstrate the effective use of genetic programming in handwritten character recognition. When the resources utilized by the classifier increase incrementally and depend on the complexity of classification task, we term such a classifier as active. The design and implementation of active classifiers based on genetic programming principles becomes very simple and efficient. Genetic Programming has helped optimize handwritten character recognition problem in terms of feature set selection. We propose an implementation with dynamism in pre-processing and classification of handwritten digit images. This paradigm will supplement existing methods by providing better performance in terms of accuracy and processing time per image for classification. Different levels of informative detail can be present in image data and our proposed paradigm helps highlight these information rich zones. We compare our performance with passive and active handwritten digit classification schemes that are based on other pattern recognition techniques.
DOI：
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发表时间：
1992
期刊：
--
影响因子：
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作者：
J. Koza
通讯作者：
J. Koza