Please use this identifier to cite or link to this item: https://doi.org/10.1109/DAS.2012.57
Title: New spatial-gradient-features for video script identification
Authors: Zhao, D.
Shivakumara, P. 
Lu, S.
Tan, C.L. 
Keywords: Dominat video text pixels
Gradient blocks
Spatial-gradient-features
Video scrpt identification
Video text blocks
Issue Date: 2012
Source: Zhao, D.,Shivakumara, P.,Lu, S.,Tan, C.L. (2012). New spatial-gradient-features for video script identification. Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012 : 38-42. ScholarBank@NUS Repository. https://doi.org/10.1109/DAS.2012.57
Abstract: In this paper, we present new features based on Spatial-Gradient-Features (SGF) at block level for identifying six video scripts namely, Arabic, Chinese, English, Japanese, Korean and Tamil. This works helps in enhancing the capability of the current OCR on video text recognition by choosing an appropriate OCR engine when video contains multi-script frames. The input for script identification is the text blocks obtained by our text frame classification method. For each text block, we obtain horizontal and vertical gradient information to enhance the contrast of the text pixels. We divide the horizontal gradient block into two equal parts as upper and lower at the centroid in the horizontal direction. Histogram on the horizontal gradient values of the upper and the lower part is performed to select dominant text pixels. In the same way, the method selects dominant pixels from the right and the left parts obtained by dividing the vertical gradient block vertically. The method combines the horizontal and the vertical dominant pixels to obtain text components. Skeleton concept is used to reduce pixel width to a single pixel to extract spatial features. We extract four features based on proximity between end points, junction points, intersection points and pixels. The method is evaluated on 770 frames of six scripts in terms of classification rate and is compared with an existing method. We have achieved 82.1% average classification rate. © 2012 IEEE.
Source Title: Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/41870
ISBN: 9780769546612
DOI: 10.1109/DAS.2012.57
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