Abstract
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 digital devices. Numerous studies have been proposed in the past and in recent years to improve handwritten digit recognition in various languages. Research on handwritten digit recognition in Arabic is limited. At present, deep learning algorithms are extremely popular in computer vision and are used to solve and address important problems, such as image classification, natural language processing, and speech recognition, to provide computers with sensory capabilities that reach the ability of humans. In this study, we propose a new approach for Arabic handwritten digit recognition by use of restricted Boltzmann machine (RBM) and convolutional neural network (CNN) deep learning algorithms. In particular, we propose an Arabic handwritten digit recognition approach that works in two phases. First, we use the RBM, which is a deep learning technique that can extract highly useful features from raw data, and which has been utilized in several classification problems as a feature extraction technique in the feature extraction phase. Then, the extracted features are fed to an efficient CNN architecture with a deep supervised learning architecture for the training and testing process. In the experiment, we used the CMATERDB 3.3.1 Arabic handwritten digit dataset for training and testing the proposed method. Experimental results show that the proposed method significantly improves the accuracy rate, with accuracy reaching 98.59%. Finally, comparison of our results with those of other studies on the CMATERDB 3.3.1 Arabic handwritten digit dataset shows that our approach achieves the highest accuracy rate.
Highlights
Handwritten digit recognition is a challenging problem in computer vision and pattern recognition; this problem has been studied intensively for many years, and numerous techniques and methods, such asK nearest neighbors (KNNs) [1], support vector machines (SVMs) [2], neural networks (NNs) [3], and convolutional NNs (CNNs) [2,4] have been proposed
restricted Boltzmann machine (RBM)–CNNisisalso alsotrained trained for a batch training size size of 70%
We fed the features learned by RBM into the CNN deep learning algorithm, which worked as the feature extraction and classification method
Summary
Handwritten digit recognition is a challenging problem in computer vision and pattern recognition; this problem has been studied intensively for many years, and numerous techniques and methods, such as. The main properties of deep learning methods are that they learn the effect and perform high-level feature extraction by use of the deep architectures in an unsupervised manner without considering the label data [25] To achieve this goal, layers of network are arranged hierarchically to form a deep architecture. Numerous digit handwritten recognition methods based on different feature extraction and classifier techniques have been developed. The authors in [4] proposed a CNN deep learning algorithm that uses an appropriate activation function and a regularization layer for Arabic handwritten digit recognition, thereby resulting in significantly improved accuracy compared to that of existing Arabic digit recognition methods.
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