集成神经网络在手写阿拉伯数字分类中的应用

Kathirvalavakumar Thangairulappan, Palaniappan Rathinasamy
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引用次数: 6

摘要

提出了一种利用分划法、Leader算法和神经网络对压缩形式的手写阿拉伯数字进行分类的方法。手写数字以矩阵形式表示。通过使用逻辑或操作合并相邻的行对来压缩矩阵表示,将其大小减少了一半。将每一行作为一个分区部分,对同一数字的同一分区分别形成聚类。利用分区簇的前导,采用分而治之的方法,利用所提出的集成神经网络进行模式识别。实验结果表明,该方法能够准确地识别出图案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensemble Neural Network in Classifying Handwritten Arabic Numerals
A method has been proposed to classify handwritten Arabic numerals in its compressed form using partitioning approach, Leader algorithm and Neural network. Handwritten numerals are represented in a matrix form. Compressing the matrix representation by merging adjacent pair of rows using logical OR operation reduces its size in half. Considering each row as a partitioned portion, clusters are formed for same partition of same digit separately. Leaders of clusters of partitions are used to recognize the patterns by Divide and Conquer approach using proposed ensemble neural network. Experimental results show that the proposed method recognize the patterns accurately.
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