Recognizing handwritten Arabic characters is among the crucial issues in the computer vision field and is characterized by its complex structure and contextual variations. This article presents a hybrid architecture that benefits from a potential features extractor (i.e., Convolution Neural Network (CNN)) and an accurate characters’ recognizer (i.e., Support Vector Machine (SVM)). In fact, we exhaustively optimized the hyperparameters of the CNN and the SVM kernels; yet, various fusion strategies between the two models were examined to ensure their maximum complementarity. The experiments performed on the AHCD database reached an accuracy of 98%, a new record for Arabic handwritten recognition models. As a result, a significant progress has been made using this hybrid model as it proves that the use of a complementary approach alongside an adequate optimization can solve major issues associated with the diverse and complicated structure of the Arabic handwritten script.