  Text segmentation in a video is drawing attention of researchers in the field
of image processing, pattern recognition and document image analysis because it
helps in annotating and labeling video events accurately. We propose a novel
idea of generating an enhanced frame from the R, G, and B channels of an input
frame by grouping high and low values using Min-Max clustering criteria. We
also perform sliding window on enhanced frame to group high and low values from
the neighboring pixel values to further enhance the frame. Subsequently, we use
k-means with k=2 clustering algorithm to separate text and non-text regions.
The fully connected components will be identified in the skeleton of the frame
obtained by k-means clustering. Concept of connected component analysis based
on gradient feature has been adapted for the purpose of symmetry verification.
The components which satisfy symmetric verification are selected to be the
representatives of text regions and they are permitted to grow to cover their
respective region fully containing text. The method is tested on variety of
video frames to evaluate the performance of the method in terms of recall,
precision and f-measure. The results show that method is promising and
encouraging.
