Color and Gradient Features for Text Segmentation from Video Frames

TL;DR

This paper introduces a novel video text segmentation method using color and gradient features, involving enhanced frame generation, clustering, and symmetry verification to accurately identify text regions.

Contribution

It presents a new approach combining color enhancement, clustering, and symmetry-based connected component analysis for improved video text segmentation.

Findings

Method achieves high recall and precision in tests.

Results are promising and encouraging.

Effective in diverse video frames.

Abstract

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.