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이재명(Jaemyung Lee),이주성(Jusung Lee),이영현(Younghyun Lee),이준수(Jaemyung Lee) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
Instance segmentation-based approach using Mask R-CNN is currently one of the leading methods for the text localization task. Different from general object detection tasks, the aspect ratio of text instances is too high to apply Mask R-CNN as it is. To simply apply Mask R-CNN for text detection yields false positives due to overgeneralized receptive fields for bounding boxes. In this paper, we propose a modified Mask R-CNN architecture for text detection. We present a method to extract features containing word-level and character-level receptive fields simultaneously. Our approach shows consistent performance improvement on MLT 2017 and Incidental Scene Text. Moreover, our method surpasses most of prior state-of-the-art text localization methods appeared in recent computer vision conferences.