Abstract
Scene text localization is a very crucial step in the issue of scene text recognition. The major challenges—such as how there are various sizes, shapes, unpredictable orientations, a wide range of colors and styles, occlusion, and local and global illumination variations—make the problem different from generic object detection. Unlike existing scene text localization methods, here we present a segmentation-based text detector which can detect an arbitrary shaped scene text by using polygon offsetting, combined with the border augmentation. This technique better distinguishes contiguous and arbitrary shaped text instances from nearby non-text regions. The quantitative experimental results on public benchmarks, ICDAR2015, ICDAR2017-MLT, ICDAR2019-MLT, and Total-Text datasets demonstrate the performance and robustness of our proposed method, compared to previous approaches which have been proposed.
Highlights
Automatic scene text localization is a key part in many practical daily life applications, such as instant language translation, autonomous driving, image retrieval, scene text understanding, and scene parsing
With the great success of convolutional neural networks (CNN) used in object detection, instance segmentation, and semantic segmentation problems, many scene text detectors based on object detection [1,2,3,4,5,6,7] and instance segmentation [8,9] have recently shown promising results
We presented a method based on semantic segmentation which can be used to localize arbitrarily-oriented text in natural scene images
Summary
Automatic scene text localization is a key part in many practical daily life applications, such as instant language translation, autonomous driving, image retrieval, scene text understanding, and scene parsing. To deal with this problem, in addition to representing the text instances using only text pixel masks, our proposed method learns the text’s outer border and offset masks.
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