| Summary: | Detection and recognition of traffic panels and their textual information are important applications of advanced driving assistance systems (ADAS). They can significantly contribute in enhancing road safety by optimizing the driving experience. However, these tasks might face two major challenges. First, the lack of suitable and good quality datasets. Second, the in-feasibility of global standardization of traffic panels in terms of shape, color and language of the written text. Present research is intensively directed toward Latin text-based panels, while other scripts such as Arabic remain quiet undervalued. In this paper, we address this issue by introducing ATTICA, a new open-source multi-task dataset, consisting of two main sub-datasets: ATTICA_Sign for traffic signs/panels detection and ATTICA_Text for Arabic text extraction/recognition. Our dataset gathers 1215 images with 3173 traffic panel boxes, 870 traffic sign boxes and 7293 Arabic text boxes. In order to examine the utility and advantages of our dataset, we adopt state-of-the-art deep learning based approaches. The conducted experiments show promising results confirming the valuable addition of our dataset in this field of research.
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