Encoder-Embedded Feature Enhancement in Convolutional Neural Networks for Arabic Handwritten Recognition

In the field of Arabic handwriting recognition, the search for models that perfectly combine efficiency and accuracy is still ongoing. This paper introduces a novel approach that harnesses the synergy between autoencoders and convolutional neural networks (CNNs) to set a new benchmark in the recognition of Arabic handwritten characters. Our method centers around an autoencoder that meticulously learns a compact representation of the characters, followed by the integration of its encoder into a CNN architecture, dubbed the Encoder-CNN. The prowess of our model is demonstrated through rigorous experiments on the Arabic Handwritten Characters Dataset (AHCD), where it achieved a best accuracy of 98.87%. These results not only underscore the model’s ability to capture the intricate nuances of Arabic script but also its robustness in generalizing to unseen data.