Arabic Handwriting Recognition System Based on Genetic Algorithm and Deep CNN Architectures
Kamline Miloud, Abdelmounaïm Moulay Lakhdar, Bendjillali Ridha Ilyas · 2021 International Conference on Decision Aid Sciences and Application (DASA) · 2021
It is vital to understand Arabic handwritten characters because of their numerous advantages and applications. This research paper proposes a non-segmented Arabic Handwritten Recognition (AHR) system. For the best results, the genetic algorithm was used to optimize the VGG-16 and ResNet-50 Deep Convolutional Neural Networks (CNN) architectures to extract features followed by the classification. Our investigation was based on the HACDB database. Finally., a comparison of the proposed method to other approaches demonstrates its efficacy and robustness. The optimized ResNet-50 architecture was able to achieve a 100% success rate.