{"title":"A Courtesy Amount Recognition System for Chinese Bank Checks","authors":"Dong Liu, Youbin Chen","doi":"10.1109/ICFHR.2012.154","DOIUrl":null,"url":null,"abstract":"In this paper, we present a complete courtesy amount recognition system for Chinese bank checks. The system takes color bank check images as input and consists of three main processing steps: numeral string extraction, segmentation & recognition, and post-processing. They focus sequentially on: detection and extraction of numeral string; segmentation and recognition of the string; and further analysis of recognition results for acceptance or rejection. Information fusion, method complementarity, multi-hypotheses generation then evaluation are three principles employed for designing algorithms in the first two modules. And logistic regression is used for post-processing. A large number of real checks collected from different banks are used for testing the system. Read rate around 82% is observed when the substitution rate is set to 1%, which corresponds to that of a human operator. The performance can also be tuned further toward a suitable balance between inaccuracy and rejection, in accordance with user preference.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Frontiers in Handwriting Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFHR.2012.154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we present a complete courtesy amount recognition system for Chinese bank checks. The system takes color bank check images as input and consists of three main processing steps: numeral string extraction, segmentation & recognition, and post-processing. They focus sequentially on: detection and extraction of numeral string; segmentation and recognition of the string; and further analysis of recognition results for acceptance or rejection. Information fusion, method complementarity, multi-hypotheses generation then evaluation are three principles employed for designing algorithms in the first two modules. And logistic regression is used for post-processing. A large number of real checks collected from different banks are used for testing the system. Read rate around 82% is observed when the substitution rate is set to 1%, which corresponds to that of a human operator. The performance can also be tuned further toward a suitable balance between inaccuracy and rejection, in accordance with user preference.