Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model.

Handwritten character recognition becomes one of the challenging research matters. More studies were presented for recognizing letters of various languages. The availability of Arabic handwritten characters databases was confined. Almost a quarter of a billion people worldwide write and speak Arabic. More historical books and files indicate a vital data set for many Arab nations written in Arabic. Recently, Arabic handwritten character recognition (AHCR) has grabbed the attention and has become a difficult topic for pattern recognition and computer vision (CV). Therefore, this study develops fireworks optimization with the deep learning-based AHCR (FWODL-AHCR) technique. The major intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language. It initially pre-processes the handwritten images to improve their quality of them. Then, the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors. Next, the deep echo state network (DESN) model is utilized to classify handwritten characters. Finally, the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance. Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique. The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches, with 99.91% and 98.94% on Hijja and AHCD datasets, respectively. [ABSTRACT FROM AUTHOR]