Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks
Handwritten digit recognition is an open problem in computer vision and pattern recognition, and solving this problem has elicited increasing interest. The main challenge of this problem is the design of an efficient method that can recognize the handwritten digits that are submitted by the user via...
Main Author: | Ali A. Alani |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2017-11-01
|
Series: | Information |
Subjects: | |
Online Access: | https://www.mdpi.com/2078-2489/8/4/142 |
Similar Items
-
Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)
by: Savita Ahlawat, et al.
Published: (2020-06-01) -
Handwritten Digits Recognition Using Neural Computing
by: Călin Enăchescu, et al.
Published: (2009-12-01) -
RECOGNITION OF ARABIC HANDWRITTEN CHARACTERS USING RESIDUAL NEURAL NETWORKS
by: Ahmad T. Al- Taani, et al.
Published: (2021-06-01) -
Deep Convolutional Self-Organizing Map Network for Robust Handwritten Digit Recognition
by: Saleh Aly, et al.
Published: (2020-01-01) -
Arabic Handwritten Alphanumeric Character Recognition Using Very Deep Neural Network
by: MohammedAli Mudhsh, et al.
Published: (2017-08-01)