{"title":"基于混合神经网络和小波变换的手写体识别","authors":"S. Sadkhan, Sabiha F. Jawad- SMIEEE","doi":"10.1109/NTCCIT.2018.8681190","DOIUrl":null,"url":null,"abstract":"This paper provides the application of Artificial Neural Network (ANN) and Wavelet Transformation (WT) into the problem of handwritten character recognition. The Design of a recognition system model that handle this problem based on applying Artificial Neural Network (ANN) of Kohenen ACON type. The feature extraction process made use of WT (the Haar Type). It’s used to extract the parametric features of the handwritten characters. The system was implemented using a database of 130 persons, 70 sample from the database were used for training, and the all 130 samples were used for testing the system. The efficiency of the system was tested using the Recognition Rate, and the results were promising.","PeriodicalId":123568,"journal":{"name":"2018 Al-Mansour International Conference on New Trends in Computing, Communication, and Information Technology (NTCCIT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Handwritten Recognition based on Hybrid ANN and Wavelet Transformation\",\"authors\":\"S. Sadkhan, Sabiha F. Jawad- SMIEEE\",\"doi\":\"10.1109/NTCCIT.2018.8681190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper provides the application of Artificial Neural Network (ANN) and Wavelet Transformation (WT) into the problem of handwritten character recognition. The Design of a recognition system model that handle this problem based on applying Artificial Neural Network (ANN) of Kohenen ACON type. The feature extraction process made use of WT (the Haar Type). It’s used to extract the parametric features of the handwritten characters. The system was implemented using a database of 130 persons, 70 sample from the database were used for training, and the all 130 samples were used for testing the system. The efficiency of the system was tested using the Recognition Rate, and the results were promising.\",\"PeriodicalId\":123568,\"journal\":{\"name\":\"2018 Al-Mansour International Conference on New Trends in Computing, Communication, and Information Technology (NTCCIT)\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Al-Mansour International Conference on New Trends in Computing, Communication, and Information Technology (NTCCIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NTCCIT.2018.8681190\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Al-Mansour International Conference on New Trends in Computing, Communication, and Information Technology (NTCCIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NTCCIT.2018.8681190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Handwritten Recognition based on Hybrid ANN and Wavelet Transformation
This paper provides the application of Artificial Neural Network (ANN) and Wavelet Transformation (WT) into the problem of handwritten character recognition. The Design of a recognition system model that handle this problem based on applying Artificial Neural Network (ANN) of Kohenen ACON type. The feature extraction process made use of WT (the Haar Type). It’s used to extract the parametric features of the handwritten characters. The system was implemented using a database of 130 persons, 70 sample from the database were used for training, and the all 130 samples were used for testing the system. The efficiency of the system was tested using the Recognition Rate, and the results were promising.