Deep Convolutional Neural Networks Transfer Learning Comparison on Arabic Handwriting Recognition System

Abstract

Around 27 languages and more than 420 million people worldwide use Arabic letters. That makes the Arabic language one of the most used languages. However, the Arabic language has a challenge, namely the difference in letters based on their position. Arabic handwriting recognition is important for various applications, such as education and communication. One example is during a pandemic when most education has turned digital, making recognizing students' Arabic handwriting difficult. This paper aims to create a model that can recognize Arabic handwriting by comparing several CNN architectures using transfer learning to classify Arabic, Hijja, and AHCD handwriting datasets. Transfer learning is a model that has been trained by previous datasets to other datasets and is suitable for use in models with small datasets because it can improve model accuracy even with small datasets. The datasets were split into 60%, 20%, and 20% for training, validation, and testing. Each model uses data augmentation and 50% dropout on a fully connected layer to reduce overfitting. Some of the CNN architectures used in this study to create Arabic writing recognition models are ResNet, DenseNet, VGG16, VGG19, InceptionV3, and MobileNet. The models were compiled and trained with various parameters. The best model achieved to classify AHCD and Hijja dataset is VGG16 with Adam optimizer and 0.0001 learning rate. Based on this research, it is expected to know the performance of the best model for classifying Arabic handwriting.