A new wavelet-Laplacian method for arbitrarily-oriented character segmentation in video text lines

Character segmentation is an important topic to improve the overall performance of text recognition methods due to low resolution, complex background and lots of visual variations in video. This paper presents a novel idea for segmenting characters from arbitrarily-oriented text lines based on wavelet and Laplacian combination. Firstly, we explore wavelet which decomposes a given input image into sub-levels like a pyramid structure for segmenting words based on the fact that as decomposition level increases, the gap between characters decreases due to the reduction in the size of the input image, which results in a single component for each word. Secondly, for each segmented word, we propose Laplacian wavelet combination in a new way to extract text candidates. Thirdly, we propose horizontal and vertical sampling for character segmentation from words. The proposed method is tested on curved, non-horizontal and horizontal text lines of video and the ICDAR 2005 natural scene dataset to evaluate its performance. A comparative study with an existing method shows that the proposed method outperforms it in terms of precision and f-measure.