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In recent years, recognition of text from natural scene image and video framehas got increased attention among the researchers due to its variouscomplexities and challenges. Because of low resolution, blurring effect,complex background, different fonts, color and variant alignment of text withinimages and video frames, etc., text recognition in such scenario is difficult.Most of the current approaches usually apply a binarization algorithm toconvert them into binary images and next OCR is applied to get the recognitionresult. In this paper, we present a novel approach based on color channelselection for text recognition from scene images and video frames. In theapproach, at first, a color channel is automatically selected and then selectedcolor channel is considered for text recognition. Our text recognitionframework is based on Hidden Markov Model (HMM) which uses Pyramidal Histogramof Oriented Gradient features extracted from selected color channel. From eachsliding window of a color channel our color-channel selection approach analyzesthe image properties from the sliding window and then a multi-label SupportVector Machine (SVM) classifier is applied to select the color channel thatwill provide the best recognition results in the sliding window. This colorchannel selection for each sliding window has been found to be more fruitfulthan considering a single color channel for the whole word image. Fivedifferent features have been analyzed for multi-label SVM based color channelselection where wavelet transform based feature outperforms others. Ourframework has been tested on different publicly available scene/video textimage datasets. For Devanagari script, we collected our own data dataset. Theperformances obtained from experimental results are encouraging and show theadvantage of the proposed method.

Text Recognition in Scene Image and Video Frame using Color Channel Selection