Identifying and acknowledging Traffic Panels (TP) and the text they display constitute significant use cases for Advanced Driver Assistance Systems (ADAS). In recent years, particularly in the context of the Arabic language, extracting textual information from TP and signs has emerged as a challenging problem in the field of computer vision. Furthermore, the significant rise in road traffic accidents within Arabic-speaking countries has resulted in substantial financial losses and loss of human lives. This is largely attributed to the limited number of diverse datasets for traffic signs and the absence of a reliable system for TP detection. Implementing warning and guidance systems for drivers on the road not only addresses this issue but also paves the way for the integration of intelligent components into future vehicles, offering decision support for transitioning to semi-automatic or fully automatic driving based on the driver’s health condition. These tasks present us with two main challenges. First, it involves developing a new Arabic dataset called the Syphax Traffic Panels dataset (STP) tailored to the diverse conditions of natural scenes gathered from “Sfax,” a city in Tunisia. This dataset aims to provide high-quality images of Arabic TP. Secondly, we suggest a deep learning method for detecting Arabic text on TP by evaluating the performance of the state-of-the-art algorithms in this context. In our study, we enhance the architecture of the most successful result achieved. The experiments conducted reveal promising results, affirming the significant contribution of our dataset to this research area, and even more encouraging results stemming from the enhancements made to the proposed method. The dataset we possess is accessible to the general public on IEEE DataPort https://dx.doi.org/10.21227/5zd9-pe55.

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