首页> 外文会议>12th International Conference on Frontiers in Handwriting Recognition >Recognition of Words from Legal Amounts of Indian Bank Cheques
【24h】

Recognition of Words from Legal Amounts of Indian Bank Cheques

机译:从印度银行支票的法律金额中识别单词

获取原文

摘要

Legal amount of Indian bank cheques contains 36 different words. Most of the Indian cheques in cities are written in English although some of them are written in Hindi and other state languages. As the legal amount words written in English can be case sensitive, the size of the lexicon for legal word recognition can go up to 108 (3´36). In this paper a lexicon driven segmentation-recognition scheme is proposed for the recognition of legal amount words from Indian bank cheques written in English. A water reservoir concept is used to pre-segment the words into primitive components and the primitive components of a word are then merged into possible characters to get the best word using the lexicon of 36 different legal words of bank cheque. To merge these primitive components into characters and to get optimum character segmentation, dynamic programming is employed using total likelihood of the characters of a word as an objective function. To calculate the likelihood of a character, Modified Quadratic Discriminant Function (MQDF) is used. The features used in the MQDF are mainly based on directional features of the contour points of the components. In the paper it is assumed that the words are already extracted from the cheque image for recognition. A database consisting of 5400 words, collected from 50 writers has been used for testing the system and an accuracy of 97.04% was observed.
机译:印度银行支票的合法金额包含36个不同的词。城市中的大多数印度支票都是用英语写的,尽管其中一些是用印地语和其他州语言写的。由于用英语写的合法单词的大小写可能会区分大小写,因此合法单词识别的词典的大小可能会增加到108(3´36)。本文提出了一种词典驱动的分段识别方案,该方案用于识别用英语编写的印度银行支票中的合法金额单词。使用水库概念将单词预分割为原始成分,然后使用36个不同的银行支票合法词汇的词典将单词的原始成分合并为可能的字符,以获得最佳单词。为了将这些原始成分合并为字符并获得最佳的字符分割,采用了以词的字符的总似然性为目标函数的动态编程。为了计算字符的可能性,使用了改进的二次判别函数(MQDF)。 MQDF中使用的特征主要基于组件轮廓点的方向特征。在本文中,假设单词已经从支票图像中提取出来以进行识别。从50位作者那里收集的5400个单词组成的数据库已用于测试系统,其准确性为97.04%。

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