First, a database of words was assembled and used as a test base. This paper proposes a system for text-dependent writer identification based on Arabic handwriting. It contains 9900 ligatures and 5500 characters written by 50 writers. This database is designed to cover all forms of Arabic characters, including ligatures. This article introduces a new database for Arabic handwritten characters and ligatures. Indeed, the recognition of Arabic handwriting characters still catches the absence of a reference database of Arabic handwritten characters covering all forms of Arabic characters and all possible ligatures of the characters. makes it possible to compare objectively the results of the different systems developed in this field. In fact, it has an undeniable interest in carrying out tasks that may be tedious in certain areas, namely the automatic processing of Arab administrative records, the digitization and safeguarding of the written Arab cultural heritage, and so on. Unlike Latin, the recognition of Arabic handwritten characters remains at the level of research and experimentation. This model's advantages with fast and light computation time bring the possibility to use this model on devices with limited computation resources such as mobile devices, familiar web server interface, and internet-of-things devices. The average inference time with the same specification mentioned above is 0.57 seconds, and again the fast inference time is because of the simplicity of the model and dataset toolbar. The model has an accuracy of 86.68% using 1000 datasets and conducted for 50 epochs based on the results. The simplicity of the training model and dataset used in this work brings the advantage of computation weight and time. One of the most popular methods lately for dealing with image classification problems is to use Deep Learning techniques, namely using the Convolutional Neural Network (CNN) method using the KERAS framework. Therefore, in this study, we try to create a Javanese character classification model hoping that this model will later be used as a basis for developing research into the next stage. The problem is that Javanese sentences are often found in Yogyakarta, especially the use of name tourist attractions, making it difficult for tourists to translate these Javanese sentences. Javanese Letters, in this case, is a basis of a sentence that uses the Javanese language. One of the essential things in research engaged in the field of Computer Vision is image classification, wherein previous studies models were used to classify an image.