A Robust Wavelet Transform Based Technique for Video Text Detection

In this paper, we propose a new method based on wavelet transform, statistical features and central moments for both graphics and scene text detection in video images. The method uses wavelet single level decomposition LH, HL and HH subbands for computing features and the computed features are fed to k means clustering to classify the text pixel from the background of the image. The average of wavelet subbands and the output of k means clustering helps in classifying true text pixel in the image. The text blocks are detected based on analysis of projection profiles. Finally, we introduce a few heuristics to eliminate false positives from the image. The robustness of the proposed method is tested by conducting experiments on a variety of images of low contrast, complex background, different fonts, and size of text in the image. The experimental results show that the proposed method outperforms the existing methods in terms of detection rate, false positive rate and misdetection rate.