SCALE-INVARIANT MULTI-ORIENTED TEXT DETECTION IN WILD SCENE IMAGE

Automatic detection of scene texts in the wild is a challenging problem, particularly due to the difficulties in handling (i) occlusions of varying percentages, (ii) widely different scales and orientations, (iii) severe degradation in the image quality etc. Here, we propose a deep architecture consisting of a novel Feature Representation Block (FRB) capable of efficient abstraction of the input information. Also, we consider curriculum learning with respect to difficulties in image samples and gradual increase in pixel-wise blurring. Our framework is capable of detecting texts of different scales and orientations. It can tackle blurring from multiple possible sources, non-uniform illumination as well as partial occlusions of varying percentages. Text detection performance of the proposed framework on various benchmark datasets including ICDAR 2015, ICDAR 2017 MLT, MSRA-TD500 and COCO-Text has significantly improved the respective state-of-the-art results.