Shuvanon Razik, E. Hossain, Sabir Ismail, Md Saiful Islam
{"title":"SUST-BHND: A database of Bangla Handwritten Numerals","authors":"Shuvanon Razik, E. Hossain, Sabir Ismail, Md Saiful Islam","doi":"10.1109/ICIVPR.2017.7890891","DOIUrl":null,"url":null,"abstract":"This paper presents the development process of the SUST-Bangla Handwritten Numeral Database (SUST-BHND). We extracted handwritten Bengali digits from twenty-one hundred pre-designed form filled by different people. After data retrieval, cleaning, processing and error analysis we have created a database consisting of 101065 sample images. It provides a basic database for Bangla OCR and script identification research field. Finally, a deep convolutional neural network was trained by the database which led to an accuracy of around 99.4%.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIVPR.2017.7890891","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
This paper presents the development process of the SUST-Bangla Handwritten Numeral Database (SUST-BHND). We extracted handwritten Bengali digits from twenty-one hundred pre-designed form filled by different people. After data retrieval, cleaning, processing and error analysis we have created a database consisting of 101065 sample images. It provides a basic database for Bangla OCR and script identification research field. Finally, a deep convolutional neural network was trained by the database which led to an accuracy of around 99.4%.