Abstract <p>Arabic handwriting recognition is a very active research area that poses a significant challenge due to the complexity and diversity of the Arabic script. Deep learning techniques are becoming increasingly popular, particularly in computer vision applications. It is a promising approach for improving pattern recognition tasks in general. This study aims to use a convolutional neural network (CNN) and a long short-term memory network (LSTM) for offline recognition of handwritten Arabic characters. A series of experiments was carried out to create an effective model for each neural network and to evaluate and compare the results of these two techniques. A combined model of the two networks was also proposed. The effectiveness of the models is assessed using the AHCD database. The results show an accuracy of 98.57%, which is good when compared to the current state of the art.</p>