A two-stage text detection approach using gradient point adjacency and deep network Online publication date: Tue, 12-Apr-2022
by Tauseef Khan; Ayatullah Faruk Mollah
International Journal of Computational Science and Engineering (IJCSE), Vol. 25, No. 2, 2022
Abstract: Scene text detection is a pivotal problem in computer vision and image processing research. In this paper, a fair attempt is made to design a simple yet effective text detection method in Indic script environment. At first, a fine-scale edge-map is generated from the original image, and subsequently, adaptive clustering is applied to form clusters of edge-points based on their spatial density. Foreground objects are extracted with the help of cluster boundaries and considered as prospective text proposals. Such text proposals are fed to a deep convolutional neural network for learning and prediction as text and non-texts. Finally, true-text components are aggregated as localised final texts of the original image. The proposed method is evaluated on two benchmark datasets viz. ICDAR 2017-MLT and ICDAR 2013 born image, and obtained results are found to surpass some other state-of-the-art methods, which demonstrates its effectiveness in scene and digital environments.
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