Mehdi Moghadam Fard, Maryam Moghadam Fard, B. Minaei-Bidgoli, M. Hussain
{"title":"Persian on-line handwritten character recognition by RCE spatio-temporal neural network","authors":"Mehdi Moghadam Fard, Maryam Moghadam Fard, B. Minaei-Bidgoli, M. Hussain","doi":"10.1145/1456223.1456246","DOIUrl":null,"url":null,"abstract":"In this paper a new Persian on-line handwritten character recognition system using neural network is presented. The proposed system is based-on a newly developed Spatio-Temporal Artificial Neuron (STAN) which is well adapted for the recognition of Spatio-Temporal patterns. In this model the strokes of a character generated by a digitizing tablet is presented in form of a sequence of spikes corresponding to displacement of the stylus. The architecture of the proposed system is based on three modules preprocessing, spike extraction and classification. The second and third modules are based on neural architectures which have STANs as their neurons. Our database comprises the handwritings of 80 persons. Each person has written 10 times each of 32 characters","PeriodicalId":309453,"journal":{"name":"International Conference on Soft Computing as Transdisciplinary Science and Technology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Soft Computing as Transdisciplinary Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1456223.1456246","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper a new Persian on-line handwritten character recognition system using neural network is presented. The proposed system is based-on a newly developed Spatio-Temporal Artificial Neuron (STAN) which is well adapted for the recognition of Spatio-Temporal patterns. In this model the strokes of a character generated by a digitizing tablet is presented in form of a sequence of spikes corresponding to displacement of the stylus. The architecture of the proposed system is based on three modules preprocessing, spike extraction and classification. The second and third modules are based on neural architectures which have STANs as their neurons. Our database comprises the handwritings of 80 persons. Each person has written 10 times each of 32 characters
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