Developing a New CCN Technique for Arabic Handwritten Digits Recognition
Hamdy Amin Morsy
Hamdy Amin Morsy, Electronics and Communications Engineering Department, Faculty of Engineering at Helwan University, Cairo, Egypt.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 520-524 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4588099320 | DOI: 10.35940/ijrte.C4588.099320
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Convolutional Neural Networks (CNN) have many applications in object recognition such as character and digit recognition. Few researches are performed on Arabic handwritten digits recognition. In this research, we will develop a new algorithm to utilize the convolutional neural networks with sigmoid function (σ-CNN) to recognize Arabic handwritten digits recognition. The performance of this method provides minimum cost functions with maximum testing accuracy results in compared to other existing techniques.
Keywords: Machine Learning, Neural Networks, Image Processing, Natural Language Processing.