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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5654
Title: Bayesian classifier for multi-oriented video text recognition system
Authors: Roy S.
Shivakumara P.
Pratim Roy, Partha
Pal U.
Tan C.L.
Lu T.
Published in: Expert Systems with Applications
Abstract: Developing an automatic system for recognizing video texts such as signboards, street names, room numbers, building names and hotels names is challenging due to low resolution, complex background, font or font size variations, and multiple orientations of texts. In this paper, we develop a new system to recognize video texts through binarization by introducing a Bayesian classifier. We explore wavelet decomposition and gradient sub-bands to enhance text information in video. The enhanced information is used in different ways to calculate the requirement of Bayesian classifier, such as a priori probability and conditional probabilities of text pixels to estimate the posterior probability automatically, which results in text components. Connected component analysis is then applied to restore missing text information before sending it to an OCR engine if any disconnection exists in the text components. Experimental results on video data, the benchmark ICDAR scene character data (camera images) and arbitrary orientation data (camera images) show that the proposed method outperforms existing baseline methods in terms of recognition rates at both character and pixel levels. ©2015 Elsevier Ltd. All rights reserved.
Citation: Expert Systems with Applications (2015), 42(13): 5554-5566
URI: https://doi.org/10.1016/j.eswa.2015.02.030
http://repository.iitr.ac.in/handle/123456789/5654
Issue Date: 2015
Publisher: Elsevier Ltd
Keywords: Bayesian classifier
Video text binarization
Video text lines
Video text recognition
Wavelet and gradient sub-bands
ISSN: 9574174
Author Scopus IDs: 57199785269
56004326900
56880478500
57200742116
15119385200
27169293700
Author Affiliations: Roy, S., Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lampur, Malaysia
Shivakumara, P., Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lampur, Malaysia
Roy, P.P., Indian Institute of Technology, Roorkee, India
Pal, U., Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India
Tan, C.L., School of Computing, National University of Singapore, Singapore, Singapore
Lu, T., National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China
Funding Details: The work is supported by the University of Malaya HIR under Grant No. M.C/625/1/HIR/210 , and the Natural Science Foundation of China under Grant Nos. 61272218 and 61321491 .
Corresponding Author: Shivakumara, P.; Faculty of Computer Science and Information Technology, University of MalayaMalaysia
Appears in Collections:Journal Publications [CS]

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