Title: Text Extraction From Texture Images Using Masked Signal Decomposition,
Abstract: Text extraction is an important problem in image processing with applications
from optical character recognition to autonomous driving. Most of the
traditional text segmentation algorithms consider separating text from a simple
background (which usually has a different color from texts). In this work we
consider separating texts from a textured background, that has similar color to
texts. We look at this problem from a signal decomposition perspective, and
consider a more realistic scenario where signal components are overlaid on top
of each other (instead of adding together). When the signals are overlaid, to
separate signal components, we need to find a binary mask which shows the
support of each component. Because directly solving the binary mask is
intractable, we relax this problem to the approximated continuous problem, and
solve it by alternating optimization method. We show that the proposed
algorithm achieves significantly better results than other recent works on
several challenging images.