A New Laplacian Method for Arbitrarily-Oriented Word Segmentation in Video

Word segmentation from video text line is challenging because video poses several challenges, such as complex background, low resolution, arbitrary orientation, etc. Besides, word segmentation is essential for improving text recognition accuracy. Therefore, we propose a novel method for segmenting words by exploring zero crossing points for each sliding window over text line. The candidate zero crossing pointes are defined based on characteristics of positive and negative Laplacian values at text region and non-text region. The percentage of candidate zero crossing points is calculated for each sliding window and is used for identifying the seed window that represents space between words. For the seed window, we propose a novel idea of horizontal and vertical sampling based on the percentage values to estimate the width and the height of the word spacing. Then the width and the height of the word spacing are used to validate the actual word spacing. Experimental results comparing with an existing method show that the proposed method is better than the existing method in terms of recall, precision and f-measure on curved, horizontal, non-horizontal, Hua´s video data, as well as ICDAR data. We also test it on our own data containing multiscript text lines to show the robustness of the proposed method.