Please use this identifier to cite or link to this item:
|Title:||Wavelet-gradient-fusion for video text binarization||Authors:||Roy, S.
|Keywords:||Video character rcognition
Video text lines
Video Video text restoration
|Issue Date:||2012||Citation:||Roy, S.,Shivakumara, P.,Roy, P.P.,Tan, C.L. (2012). Wavelet-gradient-fusion for video text binarization. Proceedings - International Conference on Pattern Recognition : 3300-3303. ScholarBank@NUS Repository.||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. © 2012 ICPR Org Committee.||Source Title:||Proceedings - International Conference on Pattern Recognition||URI:||http://scholarbank.nus.edu.sg/handle/10635/40904||ISBN:||9784990644109||ISSN:||10514651|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 16, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.