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Individuals who are deaf or those having hearing problems can communicate through alternative modes of communication, such as sign language, both within and outside their community. As a result, the sign serves as a tool that mediated communication goals. With the aid of recent advances in computer vision, there has been promising progress in the fields of motion and gesture detection using deep learning. In this work, convolutional neural networks are utilised to develop a model for recognising the Arabic language's alphabet signs in order to aid communication goal. The research used the Arabic Alphabets Sign Language Dataset (ArASL2018), which contains sets of images indicating each specific sign for each letter of the alphabet. Experimental analysis was carried out using the proposed model. The results of the analysis reveal a recognition accuracy of 94.46%. Furthermore, the comparative analysis with the previous studies indicates that the proposed model outperformed all of them in terms of recognition accuracy. [ABSTRACT FROM AUTHOR]