  Text detection and segmentation is an important prerequisite for many content
based image analysis tasks. The paper proposes a novel text extraction and
character segmentation algorithm using Maximally Stable Extremal Regions as
basic letter candidates. These regions are then subjected to thresholding and
thereafter various connected components are determined to identify separate
characters. The algorithm is tested along a set of various JPEG, PNG and BMP
images over four different character sets; English, Russian, Hindi and Urdu.
The algorithm gives good results for English and Russian character set; however
character segmentation in Urdu and Hindi language is not much accurate. The
algorithm is simple, efficient, involves no overhead as required in training
and gives good results for even low quality images. The paper also proposes
various challenges in text extraction and segmentation for multilingual inputs.
