Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2010.01.009
Title: Accurate video text detection through classification of low and high contrast images
Authors: Shivakumara, P. 
Huang, W. 
Quy Phan, T.Q. 
Lim Tan, C.L. 
Keywords: Edge analysis
Feature extraction
Filters
Heuristic rules
High contrast
Low contrast
Text detection
Issue Date: 2010
Source: Shivakumara, P., Huang, W., Quy Phan, T.Q., Lim Tan, C.L. (2010). Accurate video text detection through classification of low and high contrast images. Pattern Recognition 43 (6) : 2165-2185. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2010.01.009
Abstract: Detection of both scene text and graphic text in video images is gaining popularity in the area of information retrieval for efficient indexing and understanding the video. In this paper, we explore a new idea of classifying low contrast and high contrast video images in order to detect accurate boundary of the text lines in video images. In this work, high contrast refers to sharpness while low contrast refers to dim intensity values in the video images. The method introduces heuristic rules based on combination of filters and edge analysis for the classification purpose. The heuristic rules are derived based on the fact that the number of Sobel edge components is more than the number of Canny edge components in the case of high contrast video images, and vice versa for low contrast video images. In order to demonstrate the use of this classification on video text detection, we implement a method based on Sobel edges and texture features for detecting text in video images. Experiments are conducted using video images containing both graphic text and scene text with different fonts, sizes, languages, backgrounds. The results show that the proposed method outperforms existing methods in terms of detection rate, false alarm rate, misdetection rate and inaccurate boundary rate. © 2010 Elsevier Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/39130
ISSN: 00313203
DOI: 10.1016/j.patcog.2010.01.009
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