The detection and recognition of handwritten letters is a crucial challenge for computer vision systems, although new developments in learning algorithms have resulted in considerable advances in English writing recognition, whereas Arabic writing is generally ignored. The aim of this work is to present a notable advance in Arabic character recognition by introducing an enhanced hybrid model. Arabic characters have complex characteristics that CNN is excellent at recognizing; the SVM uses this knowledge wisely to classify the characters accurately. The capability of the SVM’s linear generalization combined with the CNN’s capacity to capture non-linear characteristics is what makes this hybrid architecture so powerful. This model is an intelligent fusion of a support vector machine (SVM) with a convolutional neural network (CNN). Extensive testing on the specialized ABHCD database has shown that this model may achieve an excellent accuracy of up to 98%. Confirmation of the method’s strength and efficacy through experiments opens the way for more complex applications, such as multilingual OCR. The invention holds great potential to advance the domains of document analysis, providing new perspectives into the development of more precise and adaptable systems.