  In recent years, recognition of text from natural scene image and video frame
has got increased attention among the researchers due to its various
complexities and challenges. Because of low resolution, blurring effect,
complex background, different fonts, color and variant alignment of text within
images and video frames, etc., text recognition in such scenario is difficult.
Most of the current approaches usually apply a binarization algorithm to
convert them into binary images and next OCR is applied to get the recognition
result. In this paper, we present a novel approach based on color channel
selection for text recognition from scene images and video frames. In the
approach, at first, a color channel is automatically selected and then selected
color channel is considered for text recognition. Our text recognition
framework is based on Hidden Markov Model (HMM) which uses Pyramidal Histogram
of Oriented Gradient features extracted from selected color channel. From each
sliding window of a color channel our color-channel selection approach analyzes
the image properties from the sliding window and then a multi-label Support
Vector Machine (SVM) classifier is applied to select the color channel that
will provide the best recognition results in the sliding window. This color
channel selection for each sliding window has been found to be more fruitful
than considering a single color channel for the whole word image. Five
different features have been analyzed for multi-label SVM based color channel
selection where wavelet transform based feature outperforms others. Our
framework has been tested on different publicly available scene/video text
image datasets. For Devanagari script, we collected our own data dataset. The
performances obtained from experimental results are encouraging and show the
advantage of the proposed method.
