Luiz Oliveira, R. Sabourin, Flávio Bortolozzi, C. Suen
{"title":"A modular system to recognize numerical amounts on Brazilian bank cheques","authors":"Luiz Oliveira, R. Sabourin, Flávio Bortolozzi, C. Suen","doi":"10.1109/ICDAR.2001.953819","DOIUrl":null,"url":null,"abstract":"The paper presents a modular system to recognize numerical amounts on Brazilian bank cheques. The system uses a segmentation-based recognition approach and the recognition function is based on a recognition and verification strategy. Our approach consists of combining the outputs from different levels such as segmentation, recognition and post-processing in a probabilistic model. A new feature set is introduced to the verifier module in order to detect segmentation effects such as over-segmentation and under-segmentation. Finally, we present experimental results on two databases: numerical amounts and NIST SD19. The latter aims at validating the concept of modular system and showing the robustness of the system over a well-known database.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Sixth International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2001.953819","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30
Abstract
The paper presents a modular system to recognize numerical amounts on Brazilian bank cheques. The system uses a segmentation-based recognition approach and the recognition function is based on a recognition and verification strategy. Our approach consists of combining the outputs from different levels such as segmentation, recognition and post-processing in a probabilistic model. A new feature set is introduced to the verifier module in order to detect segmentation effects such as over-segmentation and under-segmentation. Finally, we present experimental results on two databases: numerical amounts and NIST SD19. The latter aims at validating the concept of modular system and showing the robustness of the system over a well-known database.