Abstract
Handwriting recognition for computer systems has been in research for a long time, with different researchers having an extensive variety of methods at their disposal. The problem is that most of these experiments are done in English, as it is the most spoken language in the world. But other languages such as Arabic, Mandarin, Spanish, French, and Russian also need research done on them since there are millions of people who speak them. In this work, recognizing and developing Arabic handwritten characters is proposed by cleaning the state-of-the-art Arabic dataset called Hijaa, developing Conventional Neural Network (CNN) with a hybrid model using Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) classifiers. The CNN is used for feature extraction of the Arabic character images, which are then passed on to the Machine Learning classifiers. A recognition rate of up to 96.3% for 29classes is achieved, far surpassing the already state-of-the-art results of the Hijaa dataset.