Arabic Handwritten Digit Recognition using Convolutional Neural Network
Amsal Pardamean1, Dewy Yuliana2, Sri Watmah3, Sisferi Hikmawan4, Sfenrianto5
1Amsal Pardamean*, Master of Computer Science, Postgraduate Programs STMIK Nusa Mandiri, Jakarta, Indonesia.
2Dewy Yuliana, Informatics Engineering Department, Faculty of Computer ScienceUniversitasSriwijaya, Palembang, Indonesia.
3Sri Watmah, Master of Computer Science, Postgraduate Programs STMIK Nusa Mandiri, Jakarta, Indonesia.
4Sisferi Hikmawan, Master of Computer Science, Postgraduate Programs STMIK Nusa Mandiri, Jakarta, Indonesia.
5Sfenrianto,Information Systems Management Department, BINUS Graduate Program – Master of Information Systems Management,Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1187-1190 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7745038620/2020©BEIESP | DOI: 10.35940/ijrte.F7745.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Arabic is the most widely used language in the world, especially the Arab League Country. Of course, in those countries often use Arabic numeral in banks and business applications, postal zip code and data entry application. This research has focused on handwriting recognition of Arabic numeral that has unlimited variation in human handwriting such as style and shape. The proposed method on the deep learning technique is Convolutional Neural Network. LeNet-5 architect also used in training and recognizing the handwritten image of Arabic numeral as much as 70000 images derived from MADbase dataset. The experimental result on 10000 images of database used is by comparing the number of epoch in training process yields, and the average accuracy is 97.67%.
Keywords: Handwritten Digit Recognition, Arabic Numeral, Deep Learning, Convolutional Neural Network
Scope of the Article: Deep Learning.