...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >Writer identification system for pre-segmented offline handwritten Devanagari characters using k-NN and SVM
【24h】

Writer identification system for pre-segmented offline handwritten Devanagari characters using k-NN and SVM

机译：WRITER识别系统用于预分割的离线手写手写的Devanagari字符，使用K-NN和SVM

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A biometric identification system based on single and multiple modalities has been an evolving concept for solving criminal issues, security and privacy maintenance and for checking the authentication of an individual. The writer identification system is a type of biometric identification in which handwriting of an individual is taken as a biometric identifier. It is a system in which the writer can be identified based on his handwritten text. These systems employ machine learning and pattern recognition algorithms for the generation of a framework. In this paper, the authors have presented a novel system for the writer identification based upon the pre-segmented characters of Devanagari script and also presenting comprehensive state-of-the-art work. The experiment is performed on the corpus consisting of five copies of each character of Devanagari script written by 100 different writers, selected randomly at the public places and consisting of total 24,500 samples of Devanagari characters. Four feature extraction methodologies such as zoning, diagonal, transition and peak extent-based features and classification methods such as k-NN and linear SVM are used with identification accuracy of 91.53% when using zoning, transition and peak extent-based features with a linear SVM classifier.
机译：基于单个和多种方式的生物识别系统是解决刑事问题，安全和隐私维护以及检查个人认证的不断发展的概念。作者识别系统是一种生物识别识别类型，其中单个单独的手写被视为生物识别标识符。它是一个系统，其中可以基于他的手写文本来识别作者。这些系统采用机器学习和模式识别算法来生成框架。本文基于Devanagari脚本的预分割特征，作者呈现了作者识别的新系统，也提出了全面的最先进的工作。该实验对由100个不同作家编写的Devanagari脚本的每个角色的五个副本进行，随机在公共场所选择，并由总共24,500个Devanagari字符样本组成。四个特征提取方法，如分区，对角线，转变和基于峰值范围的特征和分类方法，如K-NN和线性SVM，在使用带状区域的分区，过渡和峰值范围的特征时，识别精度为91.53％。 SVM分类器。

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