{"title":"基于机器视觉的孟加拉钞票实时识别和分类自动系统。","authors":"R. Sajal, M. Kamruzzaman, F.A. Jewel","doi":"10.1109/ICCITECHN.2008.4803060","DOIUrl":null,"url":null,"abstract":"This paper presents an efficient machine vision algorithm for real time image analysis and recognition of different features of Bangladeshi bank notes by using an automatic banknotes sorting system. The features recognized by this algorithm include denominations, orientations and sides of the bank notes. In a mechatronic system the Bangladeshi bank notes are fed together. The system draws the notes one by one and gets their images using a CCD sensor from a specific side. Then the system determines the denomination, orientation and side of the bank notes by analyzing the images grabbed by the CCD sensor. The average recognition speed is 8 to 9 bank notes per second and the rate of success is 100% for the banknotes having no extremely fatal damage.","PeriodicalId":335795,"journal":{"name":"2008 11th International Conference on Computer and Information Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"A machine vision based automatic system for real time recognition and sorting of Bangladeshi bank notes.\",\"authors\":\"R. Sajal, M. Kamruzzaman, F.A. Jewel\",\"doi\":\"10.1109/ICCITECHN.2008.4803060\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an efficient machine vision algorithm for real time image analysis and recognition of different features of Bangladeshi bank notes by using an automatic banknotes sorting system. The features recognized by this algorithm include denominations, orientations and sides of the bank notes. In a mechatronic system the Bangladeshi bank notes are fed together. The system draws the notes one by one and gets their images using a CCD sensor from a specific side. Then the system determines the denomination, orientation and side of the bank notes by analyzing the images grabbed by the CCD sensor. The average recognition speed is 8 to 9 bank notes per second and the rate of success is 100% for the banknotes having no extremely fatal damage.\",\"PeriodicalId\":335795,\"journal\":{\"name\":\"2008 11th International Conference on Computer and Information Technology\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 11th International Conference on Computer and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCITECHN.2008.4803060\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 11th International Conference on Computer and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCITECHN.2008.4803060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A machine vision based automatic system for real time recognition and sorting of Bangladeshi bank notes.
This paper presents an efficient machine vision algorithm for real time image analysis and recognition of different features of Bangladeshi bank notes by using an automatic banknotes sorting system. The features recognized by this algorithm include denominations, orientations and sides of the bank notes. In a mechatronic system the Bangladeshi bank notes are fed together. The system draws the notes one by one and gets their images using a CCD sensor from a specific side. Then the system determines the denomination, orientation and side of the bank notes by analyzing the images grabbed by the CCD sensor. The average recognition speed is 8 to 9 bank notes per second and the rate of success is 100% for the banknotes having no extremely fatal damage.
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