HACR-MDL: HANDWRITTEN ARABIC CHARACTER RECOGNITION MODEL USING DEEP LEARNING

Abstract

Despite the enormous effort and prior research, Arabic handwritten character recognition still has a deep, wide-ranging, and untapped scope for study owing to the enormous challenges faced in this research area. The reason for such challenges is that the Arabic script comprises 28 alphabets, each of which can be written in two to four different forms depending on where it appears in a word—beginning, middle, end, or isolated. The Convolutional Neural Network (CNN or ConvNet) is a subtype of neural network that is commonly used in image classification, speech recognition, video processing, object detection, and segmentation because its built-in convolutional layer reduces the high dimensionality of images without losing significant information. Hence, the scope of this study is to examine the classification performance of various deep CNN models on offline handwritten Arabic character recognition. Based on the experimental comparative studies, this research proposes a Handwritten Arabic Character Recognition Model using Deep Learning (HACR-MDL), a modified CNN model. The proposed model is trained and tested using the AHCD dataset achieving an accuracy of 98.54%. The results achieved showed that HACR outperformed the recent research offline handwritten Arabic character recognition in terms of model complexity, speed, model parameters, and performance metrics.