首页> 外文会议>Computer Vision, Graphics and Image Processing; Lecture Notes in Computer Science; 4338 >Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier
【24h】

Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier

机译:使用二次分类器识别离线手写天琴字符

获取原文
获取原文并翻译 | 示例

摘要

Recognition of handwritten characters is a challenging task because of the variability involved in the writing styles of different individuals. In this paper we propose a quadratic classifier based scheme for the recognition of offline Devnagari handwritten characters. The features used in the classifier are obtained from the directional chain code information of the contour points of the characters. The bounding box of a character is segmented into blocks and the chain code histogram is computed in each of the blocks. Based on the chain code histogram, here we have used 64 dimensional features for recognition. These chain code features are fed to the quadratic classifier for recognition. From the proposed scheme we obtained 98.86% and 80.36% recognition accuracy on Devnagari numerals and characters, respectively. We used fivefold cross-validation technique for result computation.
机译:手写字符的识别是一项具有挑战性的任务,因为不同个人的写作风格存在差异。在本文中,我们提出了一种基于二次分类器的方案,用于离线Devnagari手写字符的识别。分类器中使用的特征是从字符轮廓点的定向链码信息中获得的。字符的边界框被分割为多个块,并在每个块中计算链码直方图。基于链码直方图,这里我们使用了64维特征进行识别。这些链码特征被馈送到二次分类器以进行识别。从提出的方案中,我们获得了对德夫纳加里语数字和字符的识别精度分别为98.86%和80.36%。我们使用五重交叉验证技术进行结果计算。

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