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Extracting texts of various size and shape from images containing multipleobjects is an important problem in many contexts, especially, in connection toe-commerce, augmented reality assistance system in natural scene, etc. Theexisting works (based on only CNN) often perform sub-optimally when the imagecontains regions of high entropy having multiple objects. This paper presentsan end-to-end text detection strategy combining a segmentation algorithm and anensemble of multiple text detectors of different types to detect text in everyindividual image segments independently. The proposed strategy involves asuper-pixel based image segmenter which splits an image into multiple regions.A convolutional deep neural architecture is developed which works on each ofthe segments and detects texts of multiple shapes, sizes, and structures. Itoutperforms the competing methods in terms of coverage in detecting texts inimages especially the ones where the text of various types and sizes arecompacted in a small region along with various other objects. Furthermore, theproposed text detection method along with a text recognizer outperforms theexisting state-of-the-art approaches in extracting text from high entropyimages. We validate the results on a dataset consisting of product images on ane-commerce website.

Semi-Bagging Based Deep Neural Architecture to Extract Text from High Entropy Images