利用分区和多层感知器介绍巴厘文字手写

I. A. Wiguna, Agus Muliantara
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引用次数: 0

摘要

笔迹鉴定只是众多研究中的一项。在其开发过程中,用户可以通过鼠标实时书写笔迹(在线字符识别)。对传统汉字手写识别的各种研究不断发展。其中之一是对巴厘文字的识别。与其他地区相比,巴厘文字有其独特的特点。这些汉字的形状与其他汉字的区别是相当相似的,或者有一些汉字只能通过一个小草图或涂鸦来区分。本研究使用人工神经网路与反向传播演算法进行巴厘文字识别与区划,作为特征提取的方法。在一种不同的提取方法中,使用的特征是图像质心和区域(ICZ)、区域质心和区域(ZCZ)以及特征的归一化。在这三种方法中,确定最适合巴厘文字识别的方法。从提取方法的测试结果来看,结合ICZ、ZCZ特征和归一化特征对巴厘文字的识别效果最好。在线测试结果的准确率为71.28%,离线测试的准确率为72.31%,参数为Backpropagation,使用学习率为0.03,动量值为0.5,隐藏层神经元数为130。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Introduction of Balinese Script Handwriting Using Zoning and Multilayer Perceptron
Handwriting identification is one out of the many research ever conducted. In its development, the handwriting can be written in real time by the user by using the mouse (online character recognition). Various studies on the traditional character handwriting recognition continue to be developed. One of them is the recognition of the Balinese characters. Balinese characters have their own unique characters compared with the other regions. The difference between the shapes of the characters with the other characters are quite similar, or there are some characters that can only be distinguished by a small sketch or doodle.This study uses Artificial Neural Network with Backpropagation algorithm to perform the Balinese characters recognition and zoning as a method of feature extraction. In a variation of the extraction method, the characteristics used are Image Centroid and Zone (ICZ), Zone Centroid and Zone (ZCZ) and normalization of features. Of the three methods, it will be determined the best method used in the Balinese characters recognition.From the test results of the extraction method, the combined characteristics of the ICZ, ZCZ and normalization of features were the most effective to be used for the recognition of the Balinese characters. The level of accuracy obtained from the results of the online testing was 71,28% and 72,31% for offline testing, with parameters of Backpropagation, which used the value of learning rate of 0,03, a momentum value of 0,5 and the number of neurons in the hidden layer of 130.
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