Piece-Wise Linearity Based Method for Text Frame Classication in Video
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Author(s)
Sharma, Nabin
Shivakumara, Palaiahnakote
Pal, Umapada
Blumenstein, Michael
Tan, Chew Lim
Year published
2015
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Show full item recordAbstract
The aim of text frame classification technique is to label a video frame as text or non-text before text detection and recognition. It is an essential step prior to text detection because text detection methods assume the input to be a text frame. Consequently, when a non-text frame is subjected to text detection, the precision of the text detection method decreases because of false positives. In this paper a new text frame classification approach based on component linearity is proposed. The method firstly obtains probable text clusters from the gradient values of the RGB images of an input video frame. The Sobel edges ...
View more >The aim of text frame classification technique is to label a video frame as text or non-text before text detection and recognition. It is an essential step prior to text detection because text detection methods assume the input to be a text frame. Consequently, when a non-text frame is subjected to text detection, the precision of the text detection method decreases because of false positives. In this paper a new text frame classification approach based on component linearity is proposed. The method firstly obtains probable text clusters from the gradient values of the RGB images of an input video frame. The Sobel edges corresponding to the text cluster are then extracted and used for further processing. Next, the method proposes to eliminate false text components before undertaking a linearity check where the linearity of the text components is determined using their centroids in a piece-wise manner. If the components in a frame satisfy the defined linearity condition, then the frame is considered as a text frame; otherwise it is considered as a non-text frame. The proposed method has been tested on standard text and non-text datasets of different orientations to demonstrate that it is independent of orientation. A comparative study with the existing method shows that the proposed method is superior in terms of classification rate and processing time.
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View more >The aim of text frame classification technique is to label a video frame as text or non-text before text detection and recognition. It is an essential step prior to text detection because text detection methods assume the input to be a text frame. Consequently, when a non-text frame is subjected to text detection, the precision of the text detection method decreases because of false positives. In this paper a new text frame classification approach based on component linearity is proposed. The method firstly obtains probable text clusters from the gradient values of the RGB images of an input video frame. The Sobel edges corresponding to the text cluster are then extracted and used for further processing. Next, the method proposes to eliminate false text components before undertaking a linearity check where the linearity of the text components is determined using their centroids in a piece-wise manner. If the components in a frame satisfy the defined linearity condition, then the frame is considered as a text frame; otherwise it is considered as a non-text frame. The proposed method has been tested on standard text and non-text datasets of different orientations to demonstrate that it is independent of orientation. A comparative study with the existing method shows that the proposed method is superior in terms of classification rate and processing time.
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Journal Title
Pattern Recognition
Volume
48
Issue
3
Copyright Statement
© 2015 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
Subject
Image processing
Information systems