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These regions are then labeled as textual or non-textual ROI. This identification is based on a structural approach. The textual ROIs are grouped to constitute zones according to spatial relations between them. Finally, the textual or non-textual nature of the constituted zones is refined. This last identification is based on handcrafted features and on features built from a Convolutional Neural Network (CNN) after learning. The proposed method was evaluated on the databases used for text detection in natural scene images: the competitions organized in 2017 edition of the International Conference on Document Analysis and Recognition (ICDAR2017), the Urdu-text database and our Natural Scene Image Database for Arabic Text detection (NSIDAT) database. The obtained experimental results seem to be interesting. <\/jats:p>","DOI":"10.1142\/s0219467823500109","type":"journal-article","created":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T09:47:22Z","timestamp":1639820842000},"source":"Crossref","is-referenced-by-count":2,"title":["A New Method for Arabic Text Detection in Natural Scene Images"],"prefix":"10.1142","volume":"23","author":[{"given":"Houda","family":"Gaddour","sequence":"first","affiliation":[{"name":"Miracl Laboratory-ISIMS University of Sfax Sfax, Tunisia"}]},{"given":"Slim","family":"Kanoun","sequence":"additional","affiliation":[{"name":"Universit\u00e9 of Paris Lipade, F-75006 Paris, France"}]},{"given":"Nicole","family":"Vincent","sequence":"additional","affiliation":[{"name":"Universit\u00e9 of Paris Lipade, F-75006 Paris, France"}]}],"member":"219","published-online":{"date-parts":[[2021,12,17]]},"reference":[{"key":"S0219467823500109BIB001","first-page":"258","volume-title":"Int. Conf. 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