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In addition to the benefits provided by deep networks, the system is protected against over-fitting. Experimentally, the authors demonstrated that the extracted features are effective for handwritten character recognition and show very good performance comparable to the state of the art on handwritten text recognition. Yet using Dropout, the proposed CDBN architectures achieved a promising accuracy rates of 91.55% and 98.86% when applied to IFN\/ENIT and HACDB databases, respectively.<\/jats:p>","DOI":"10.4018\/ijmdem.2019100102","type":"journal-article","created":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T20:05:17Z","timestamp":1576181117000},"page":"26-45","source":"Crossref","is-referenced-by-count":15,"title":["Boosting of Deep Convolutional Architectures for Arabic Handwriting Recognition"],"prefix":"10.4018","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4702-7692","authenticated-orcid":true,"given":"Mohamed","family":"Elleuch","sequence":"first","affiliation":[{"name":"National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia"}]},{"given":"Monji","family":"Kherallah","sequence":"additional","affiliation":[{"name":"Faculty of Sciences, University of Sfax, Sfax, Tunisia"}]}],"member":"2432","reference":[{"key":"IJMDEM.2019100102-0","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-1824-0"},{"issue":"3","key":"IJMDEM.2019100102-1","first-page":"304","article-title":"Handwriting Arabic character recognition LeNet using neural network.","volume":"6","author":"R.Al-Jawfi","year":"2009","journal-title":"The International Arab Journal of Information Technology"},{"key":"IJMDEM.2019100102-2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2011.02.006"},{"key":"IJMDEM.2019100102-3","unstructured":"Applied Media Analysis. 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