Citation

Akallouch M, Boujemaa KS, Bouhoute A, Fardousse K, Berrada I. IEEE Trans. Intel. Transp. Syst. 2022; 23(4): 3026-3036.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2020.3029451

PMID

unavailable

Abstract

The extraction of text information from traffic panels is one of the challenging problems in computer vision. Although the past decade has seen a promising shift and important progress in object detection, few works and a limited number of datasets focus specifically on extracting text from traffic signs. To address the lack of data for text detection in traffic panels, especially those with Arabic scripts, this paper introduces a new multilingual and multipurpose dataset named ASAYAR. It consists of three sub-datasets: Arabic-Latin scene text localization, traffic sign detection, and directional symbol detection. The dataset contains 1763 images collected on different Moroccan highways, and annotated manually, using 16 object categories. The fully annotated ASAYAR images contains more than 20000 bounding box objects. The paper also investigates the usability of the dataset, by evaluating the performance of the state-of-the-art algorithms for object and text detection. Experimental results show good detection scores, demonstrating the potential contribution of ASAYAR in the development of methods for text extraction from traffic panels.

Language: en

Keywords

Annotations; Arabic script; Artificial intelligence; highway traffic signs; Meteorology; Roads; Scene text extraction; Text recognition; Vehicles