首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network
【24h】

Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network

机译:使用多层聚类神经网络离线识别完全不受约束的手写数字

获取原文
获取原文并翻译 | 示例

摘要

In this paper, we propose a new scheme for off-line recognition of totally unconstrained handwritten numerals using a simple multilayer cluster neural network trained with the backpropagation algorithm and show that the use of genetic algorithms avoids the problem of finding local minima in training the multilayer cluster neural network with gradient descent technique, and improves the recognition rates. In the proposed scheme, Kirsch masks are adopted for extracting feature vectors and a three-layer cluster neural network with five independent subnetworks is developed for classifying similar numerals efficiently. In order to verify the performance of the proposed multilayer cluster neural network, experiments with handwritten numeral database of Concordia University of Canada, that of Electro-Technical Laboratory of Japan, and that of Electronics and Telecommunications Research Institute of Korea were performed. For the case of determining the initial weights using a genetic algorithm, 97.10%, 99.12%, and 99.40% correct recognition rates were obtained, respectively.
机译:在本文中,我们提出了一种使用反向传播算法训练的简单多层簇神经网络离线识别完全不受约束的手写数字的方案,并表明遗传算法的使用避免了在训练多层时发现局部最小值的问题利用梯度下降技术对神经网络进行聚类,提高了识别率。在该方案中,采用Kirsch掩模提取特征向量,并开发了具有五个独立子网的三层聚类神经网络,以有效地对相似数字进行分类。为了验证所提出的多层聚类神经网络的性能,使用加拿大康科迪亚大学的手写数字数据库,日本电子技术实验室以及韩国电子与电信研究所进行了实验。对于使用遗传算法确定初始权重的情况,分别获得了97.10%,99.12%和99.40%的正确识别率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号