Wavelet-Gradient-Fusion for Video Text Binarization
Identifieur interne : 000220 ( Main/Exploration ); précédent : 000219; suivant : 000221Wavelet-Gradient-Fusion for Video Text Binarization
Auteurs : Sangheeta Roy [Inde] ; Palaiahnakote Shivakumara [Singapour] ; Partha Pratim Roy [France] ; Chew Lim Tan [Singapour]Source :
Abstract
Achieving good character recognition rate in video images is not as easy as achieving the same from the scanned documents because of low resolution and complex background in video images. In this paper, we propose a new method using fusion of horizontal, vertical and diagonal information obtained by the wavelet and the gradient on text line images to enhance the text information. We apply k-means with k=2 on row-wise and column-wise pixels separately to extract possible text information. The union operation on row-wise and column-wise clusters provides the text candidates information. With the help of Canny of the input image, the method identifies the disconnections based on mutual nearest neighbor criteria on end points and it compares the disconnected area with the text candidates to restore the missing information. Next, the method uses connected component analysis to merge some subcomponents based on nearest neighbor criteria. The foreground (text) and background (non-text) is separated based on new observation that the color values at edge pixel of the components are larger than the color values of the pixel inside the component. Finally, we use Google Tesseract OCR to validate our results and the results are compared with the baseline thresholding techniques to show that the proposed method is superior to existing methods in terms of recognition rate on 236 video and 258 ICDAR 2003 text lines. Keywords- Wavelet-Gradient-Fusion, Video text lines, Video Video text restoration, Video character rcognition
Url:
Affiliations:
- France, Inde, Singapour
- Centre-Val de Loire, Région Centre
- Tours
- Centre Val de Loire Université, Université François-Rabelais de Tours, Université nationale de Singapour
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- to stream Main, to step Curation: 000220
Le document en format XML
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type="abstract" xml:lang="en">Achieving good character recognition rate in video images is not as easy as achieving the same from the scanned documents because of low resolution and complex background in video images. In this paper, we propose a new method using fusion of horizontal, vertical and diagonal information obtained by the wavelet and the gradient on text line images to enhance the text information. We apply k-means with k=2 on row-wise and column-wise pixels separately to extract possible text information. The union operation on row-wise and column-wise clusters provides the text candidates information. With the help of Canny of the input image, the method identifies the disconnections based on mutual nearest neighbor criteria on end points and it compares the disconnected area with the text candidates to restore the missing information. Next, the method uses connected component analysis to merge some subcomponents based on nearest neighbor criteria. The foreground (text) and background (non-text) is separated based on new observation that the color values at edge pixel of the components are larger than the color values of the pixel inside the component. Finally, we use Google Tesseract OCR to validate our results and the results are compared with the baseline thresholding techniques to show that the proposed method is superior to existing methods in terms of recognition rate on 236 video and 258 ICDAR 2003 text lines. Keywords- Wavelet-Gradient-Fusion, Video text lines, Video Video text restoration, Video character rcognition</div></front></TEI><affiliations><list><country><li>France</li><li>Inde</li><li>Singapour</li></country><region><li>Centre-Val de Loire</li><li>Région Centre</li></region><settlement><li>Tours</li></settlement><orgName><li>Centre Val de Loire Université</li><li>Université François-Rabelais de Tours</li><li>Université nationale de Singapour</li></orgName></list><tree><country name="Inde"><noRegion><name sortKey="Roy, Sangheeta" sort="Roy, Sangheeta" uniqKey="Roy S" first="Sangheeta" last="Roy">Sangheeta Roy</name></noRegion></country><country name="Singapour"><noRegion><name sortKey="Shivakumara, Palaiahnakote" sort="Shivakumara, Palaiahnakote" uniqKey="Shivakumara P" first="Palaiahnakote" last="Shivakumara">Palaiahnakote Shivakumara</name></noRegion><name sortKey="Tan, Chew Lim" sort="Tan, Chew Lim" uniqKey="Tan C" first="Chew Lim" last="Tan">Chew Lim Tan</name></country><country name="France"><region name="Région Centre"><name sortKey="Roy, Partha Pratim" sort="Roy, Partha Pratim" uniqKey="Roy P" first="Partha Pratim" last="Roy">Partha Pratim Roy</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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