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Automatic detection of scene texts in the wild is a challenging problem,particularly due to the difficulties in handling (i) occlusions of varyingpercentages, (ii) widely different scales and orientations, (iii) severedegradations in the image quality etc. In this article, we propose a fullyconvolutional neural network architecture consisting of a novel FeatureRepresentation Block (FRB) capable of efficient abstraction of information. Theproposed network has been trained using curriculum learning with respect todifficulties in image samples and gradual pixel-wise blurring. It is capable ofdetecting texts of different scales and orientations suffered by blurring frommultiple possible sources, non-uniform illumination as well as partialocclusions of varying percentages. Text detection performance of the proposedframework on various benchmark sample databases including ICDAR 2015, ICDAR2017 MLT, COCO-Text and MSRA-TD500 improves respective state-of-the-art resultssignificantly. Source code of the proposed architecture will be made availableat github.

Scale-Invariant Multi-Oriented Text Detection in Wild Scene Images