ASAYAR: A Dataset for Arabic-Latin Scene Text Localization in Highway Traffic Panels
| Autor: | Mohammed Akallouch, Afaf Bouhoute, Kaoutar Sefrioui Boujemaa, Ismail Berrada, Khalid Fardousse |
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| Rok vydání: | 2022 |
| Předmět: |
Focus (computing)
Computer science business.industry Arabic Mechanical Engineering Usability computer.software_genre Object (computer science) Object detection Symbol (chemistry) language.human_language Computer Science Applications Scripting language Minimum bounding box Automotive Engineering language Artificial intelligence business computer Natural language processing |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 23:3026-3036 |
| ISSN: | 1558-0016 1524-9050 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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