A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video

The present paper presents one of the efficient approaches toward multilingual text detection for video indexing. In this paper, we propose a method for detecting textlocated in varying and complex background in images/video. The present approach comprises four stages: In the first stage, combination of wavelet transform and Gabor filter is applied. By applying single level 2D wavelet decomposition with Gabor Filter, the intrinsic features comprising sharpen edges and texture features of an input image are obtained. In the second stage, the resultant Gabor image is classified using k-means clustering algorithm. In the third stage, morphological operations are performed on clustered pixels. Then a concept of linked list approach is used to build a true textline sequence of connected components. In the final stage, wavelet entropy of an input image is measured by signifying the complexity of unsteady signals corresponding to the position of textline sequence of connected components in leading to determine the true text region of an input image. The performance of the approach is exhibited by presenting promising experimental results for 101 video images, standard ICDAR 2003 Scene Trial Test dataset, ICDAR 2013 dataset and on our own collected South Indian Language dataset.