Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/40904
DC FieldValue
dc.titleWavelet-gradient-fusion for video text binarization
dc.contributor.authorRoy, S.
dc.contributor.authorShivakumara, P.
dc.contributor.authorRoy, P.P.
dc.contributor.authorTan, C.L.
dc.date.accessioned2013-07-04T08:15:03Z
dc.date.available2013-07-04T08:15:03Z
dc.date.issued2012
dc.identifier.citationRoy, 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.
dc.identifier.isbn9784990644109
dc.identifier.issn10514651
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40904
dc.description.abstractAchieving 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.
dc.sourceScopus
dc.subjectVideo character rcognition
dc.subjectVideo text lines
dc.subjectVideo Video text restoration
dc.subjectWavelet-Gradient-Fusion
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleProceedings - International Conference on Pattern Recognition
dc.description.page3300-3303
dc.description.codenPICRE
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Page view(s)

89
checked on Jul 9, 2021

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.