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