Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm

Puspalata Pujari, B. Majhi
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引用次数: 15

Abstract

This article aims to recognize Odia handwritten digits using gradient-based feature extraction techniques and Clonal Selection Algorithm-based (CSA) multilayer artificial neural network (MANN) classifier. For the extraction of features which contribute the most towards recognition from images, are extracted using gradient-based feature extraction techniques. Principal component analysis (PCA) is used for dimensionality reduction of extracted features. A MANN is used as a classifier for classification purposes. The weights of the MANN are adjusted using the CSA to get optimized set of weights. The proposed model is applied on Odia handwritten digits taken from the Indian Statistical Institution (ISI), Calcutta, which consists of four thousand samples. The results obtained from the experiment are compared with a genetic-based multi-layer artificial neural network (GA-MANN) model. The recognition accuracy of the CSA-MANN model is found to be 90.75%.
基于梯度特征提取和克隆选择算法的Odia手写体数字识别
本文旨在利用基于梯度的特征提取技术和基于克隆选择算法(CSA)的多层人工神经网络(MANN)分类器对Odia手写体数字进行识别。对于从图像中提取对识别贡献最大的特征,采用基于梯度的特征提取技术进行提取。采用主成分分析(PCA)对提取的特征进行降维处理。MANN是用于分类目的的分类器。利用CSA对MANN的权值进行调整,得到最优的权值集。所提出的模型应用于来自加尔各答印度统计机构(ISI)的4000个样本的Odia手写数字。实验结果与基于遗传的多层人工神经网络(GA-MANN)模型进行了比较。CSA-MANN模型的识别准确率为90.75%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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