{"title":"Recognition of container code characters through gray-level feature extraction and gradient-based classifier optimization","authors":"M. Goccia, M. Bruzzo, C. Scagliola, S. Dellepiane","doi":"10.1109/ICDAR.2003.1227804","DOIUrl":null,"url":null,"abstract":"This paper describes the recognition of container codecharacters in the project Mocont-II, where containerimages are taken in largely varying light situations. Therecognition system has to deal with gray-level charactersshowing a wide variability of brightness and contrast,varying inclination, segmentation uncertainties, damagedcharacters and the presence of shadows. Different sets offeatures were extracted directly from gray-level images,and a minimum distance classifier with a weighted metricwas used for recognition. To achieve good recognitionperformances, the feature weights and the prototype setswere optimized by a new gradient-based learningalgorithm that maximizes a fuzzy recognition ratefunctional.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2003.1227804","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
This paper describes the recognition of container codecharacters in the project Mocont-II, where containerimages are taken in largely varying light situations. Therecognition system has to deal with gray-level charactersshowing a wide variability of brightness and contrast,varying inclination, segmentation uncertainties, damagedcharacters and the presence of shadows. Different sets offeatures were extracted directly from gray-level images,and a minimum distance classifier with a weighted metricwas used for recognition. To achieve good recognitionperformances, the feature weights and the prototype setswere optimized by a new gradient-based learningalgorithm that maximizes a fuzzy recognition ratefunctional.