{"title":"基于区域加权行程长度特征的离线手写德文数字识别","authors":"P. Singh, Supratim Das, R. Sarkar, M. Nasipuri","doi":"10.1109/ICCECE.2016.8009567","DOIUrl":null,"url":null,"abstract":"Recognition of handwritten Roman characters and numerals has been extensively studied in the last few decades and its accuracy reached to a satisfactory state. But the same cannot be said while talking about the Devanagari script which is one of most popular script in India. This paper proposes an efficient digit recognition system for handwritten Devanagari script. The system uses a novel 196-element Mask Oriented Directional (MOD) features for the recognition purpose. The methodology is tested using five conventional classifiers on 6000 handwritten digit samples. On applying 3-fold cross-validation scheme, the proposed system yields the highest recognition accuracy of 95.02% using Support Vector Machine (SVM) classifier.","PeriodicalId":414303,"journal":{"name":"2016 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Recognition of offline handwriten Devanagari numerals using regional weighted run length features\",\"authors\":\"P. Singh, Supratim Das, R. Sarkar, M. Nasipuri\",\"doi\":\"10.1109/ICCECE.2016.8009567\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recognition of handwritten Roman characters and numerals has been extensively studied in the last few decades and its accuracy reached to a satisfactory state. But the same cannot be said while talking about the Devanagari script which is one of most popular script in India. This paper proposes an efficient digit recognition system for handwritten Devanagari script. The system uses a novel 196-element Mask Oriented Directional (MOD) features for the recognition purpose. The methodology is tested using five conventional classifiers on 6000 handwritten digit samples. On applying 3-fold cross-validation scheme, the proposed system yields the highest recognition accuracy of 95.02% using Support Vector Machine (SVM) classifier.\",\"PeriodicalId\":414303,\"journal\":{\"name\":\"2016 International Conference on Computer, Electrical & Communication Engineering (ICCECE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Computer, Electrical & Communication Engineering (ICCECE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCECE.2016.8009567\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCECE.2016.8009567","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognition of offline handwriten Devanagari numerals using regional weighted run length features
Recognition of handwritten Roman characters and numerals has been extensively studied in the last few decades and its accuracy reached to a satisfactory state. But the same cannot be said while talking about the Devanagari script which is one of most popular script in India. This paper proposes an efficient digit recognition system for handwritten Devanagari script. The system uses a novel 196-element Mask Oriented Directional (MOD) features for the recognition purpose. The methodology is tested using five conventional classifiers on 6000 handwritten digit samples. On applying 3-fold cross-validation scheme, the proposed system yields the highest recognition accuracy of 95.02% using Support Vector Machine (SVM) classifier.