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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5633
Title: Multi-oriented text detection and verification in video frames and scene images
Authors: Sain A.
Bhunia A.K.
Pratim Roy, Partha
Pal U.
Published in: Neurocomputing
Abstract: In this paper, we bring forth a novel approach of video text detection using Fourier–Laplacian filtering in the frequency domain that includes a verification technique using Hidden Markov Model (HMM). The proposed approach deals with the text region appearing not only in horizontal or vertical directions, but also in any other oblique or curved orientation in the image. Until now only a few methods have been proposed that look into curved text detection in video frames, wherein lies our novelty. In our approach, we first apply Fourier–Laplacian transform on the image followed by an ideal Laplacian–Gaussian filtering. Thereafter K-means clustering is employed to obtain the asserted text areas depending on a maximum difference map. Next, the obtained connected components (CC) are skeletonized to distinguish various text strings. Complex components are disintegrated into simpler ones according to a junction removal algorithm followed by a concatenation performed on possible combination of the disjoint skeletons to obtain the corresponding text area. Finally these text hypotheses are verified using HMM-based text/non-text classification system. False positives are thus eliminated giving us a robust text detection performance. We have tested our framework in multi-oriented text lines in four scripts, namely, English, Chinese, Devanagari and Bengali, in video frames and scene texts. The results obtained show that proposed approach surpasses existing methods on text detection. © 2017 Elsevier B.V.
Citation: Neurocomputing (2018), 275(): 1531-1549
URI: https://doi.org/10.1016/j.neucom.2017.09.089
http://repository.iitr.ac.in/handle/123456789/5633
Issue Date: 2018
Publisher: Elsevier B.V.
Keywords: Fourier–Laplacian
Hidden Markov model
Scene text and video text retrieval
Skeletonization
Text extraction
ISSN: 9252312
Author Scopus IDs: 57195999376
57188719920
56880478500
57200742116
Author Affiliations: Sain, A., Dept. of EE, Institute of Engineering & Management, Kolkata, India
Bhunia, A.K., Dept. of ECE, Institute of Engineering & Management, Kolkata, India
Roy, P.P., Dept. of CSE, Indian Institute of Technology Roorkee, Roorkee, India
Pal, U., CVPR Unit, Indian Statistical Institute, Kolkata, India
Corresponding Author: Roy, P.P.; Dept. of CSE, Indian Institute of Technology RoorkeeIndia; email: proy.fcs@iitr.ac.in
Appears in Collections:Journal Publications [CS]

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