research-article

A new wavelet-median-moment based method for multi-oriented video text detection

Authors:

Palaiahnakote Shivakumara,

Umapada PalAuthors Info & Claims

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

Pages 279 - 286

https://doi.org/10.1145/1815330.1815366

Published: 09 June 2010 Publication History

Abstract

In this paper, we present a new method based on wavelet-median-moments and a novel idea of angle projection for detecting multi-oriented text in video. The proposed method uses wavelet decomposition first to obtain three high frequency sub-bands (LH, HL and HH) and then median moments are computed on the average sub-bands of the three high frequency sub-bands to brighten the text pixels. K-means clustering (K=2) is used for obtaining text pixels from the wavelet-median-moments features (WMMF). Text candidates are obtained by mapping the output of K-means on Sobel edge map of the original input frame. To deal with multi-oriented text, we introduce a new idea of Angle Projection (AP) based on boundary growing and nearest neighbor concepts from the text candidates instead of conventional projection profiles. The proposed method is experimented on horizontal text data, non-horizontal text data, temporal data, non-text data and camera based images (scene text data of ICDAR 2003 competition) to show that the proposed method is superior to existing methods.

References

[1]

D. Crandall and R. Kasturi, Robust Detection of Stylized Text Events in Digital Video, ICDAR 2001, pp 865--869.

Digital Library

[2]

K. Jung, "Neural network-based text location in color images", Pattern Recognition Letters 22, 2001, pp 1503--1515.

Digital Library

[3]

J. Zang and R. Kasturi. Extraction of Text Objects in Video Documents: Recent Progress, DAS 2008, pp 5--17.

Digital Library

[4]

K. Jung, K. I. Kim and A. K. Jain. Text information extraction in images and video: a survey. Pattern Recognition, 37, 2004, pp. 977--997.

[5]

V. Y. Marinano and R. Kasturi, "Locating Uniform-Colored Text in Video Frames", 15th ICPR, Volume 4, 2000, pp 539--542.

[6]

Q. Ye, Q. Huang, W. Gao and D. Zhao. Fast and robust text detection in images and video frames. Image and Vision Computing 23, 2005, pp 565--576.

Digital Library

[7]

A. K. Jain and B. Yu. Automatic Text Location in Images and Video Frames. Pattern Recognition, 31, 1998, pp 2055--2076.

[8]

C. Liu, C. Wang and R. Dai. Text Detection in Images Based on Unsupervised Classification of Edge-based Features. ICDAR 2005, pp 610--614.

Digital Library

[9]

P. Shivakumara, W. Huang and C. L. Tan. An Efficient Edge based Technique for Text Detection in Video Frames, DAS 2008, pp 307--314.

Digital Library

[10]

M. Cai, J. Song and M. R. Lyu, "A New Approach for Video Text Detection" ICIP, 2002, pp 117--120.

[11]

E. K. Wong and M. Chen. A new robust algorithm for video text extraction. Pattern Recognition 36, 2003, pp 1397--1406.

[12]

T. Q. Phan, P. Shivakumara and C. L Tan, "A Laplacian Method for Video Text Detection", ICDAR, 2009, pp 66--70.

Digital Library

[13]

H. Li, D. Doermann and O. Kia. Automatic Text Detection and Tracking in Digital Video. IEEE Transactions on Image Processing, Vol. 9, No. 1, January 2000, pp 147--156.

Digital Library

[14]

P. Shivakumara, T. Q. Phan and C. L Tan, "A Robust Wavelet Transform Based Technique for Video Text Detection", ICDAR, 2009, pp 1285--1289.

Digital Library

[15]

X. Chen and A. L. Yuille, "Detecting and Reading Text in Natural Scenes", CVPR, 2004, pp 366--373.

Digital Library

[16]

J. Zhang, D. Goldgof and R. Kasturi, "A New Edge-Based Text Verification Approach for Video", ICPR, 2008.

Cited By

Manjunath Aradhya VBasavaraju HGuru D(2019)Decade research on text detection in images/videos: a reviewEvolutionary Intelligence10.1007/s12065-019-00248-zOnline publication date: 6-Jun-2019
https://doi.org/10.1007/s12065-019-00248-z
Yuan JWei BLiu YZhang YWang L(2015)A method for text line detection in natural imagesMultimedia Tools and Applications10.1007/s11042-013-1702-774:3(859-884)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1007/s11042-013-1702-7
Wei BZhang YYuan JLiu YWang L(2014)A Novel Approach to Text Detection and Extraction from Videos by Discriminative Features and DensityChinese Journal of Electronics10.23919/CJE.2014.1085188223:2(322-328)Online publication date: Apr-2014
https://doi.org/10.23919/CJE.2014.10851882
Show More Cited By

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cover image ACM Other conferences

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

June 2010

490 pages

ISBN:9781605587738

DOI:10.1145/1815330

General Chairs:
David Doermann
University of Maryland, College Park
,
Venu Govindaraju
University at Buffalo, SUNY
,
Daniel Lopresti
Lehigh University
,
Prem Natarajan
Raytheon BBN Technologies

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2010

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DAS '10

DAS '10: The Eighth IAPR International Workshop on Document Analysis Systems

June 9 - 11, 2010

Massachusetts, Boston, USA

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Cited By

Manjunath Aradhya VBasavaraju HGuru D(2019)Decade research on text detection in images/videos: a reviewEvolutionary Intelligence10.1007/s12065-019-00248-zOnline publication date: 6-Jun-2019
https://doi.org/10.1007/s12065-019-00248-z
Yuan JWei BLiu YZhang YWang L(2015)A method for text line detection in natural imagesMultimedia Tools and Applications10.1007/s11042-013-1702-774:3(859-884)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1007/s11042-013-1702-7
Wei BZhang YYuan JLiu YWang L(2014)A Novel Approach to Text Detection and Extraction from Videos by Discriminative Features and DensityChinese Journal of Electronics10.23919/CJE.2014.1085188223:2(322-328)Online publication date: Apr-2014
https://doi.org/10.23919/CJE.2014.10851882
Lu TPalaiahnakote STan CLiu WLu TPalaiahnakote STan CLiu W(2014)Video Caption DetectionVideo Text Detection10.1007/978-1-4471-6515-6_3(49-80)Online publication date: 30-Jun-2014
https://doi.org/10.1007/978-1-4471-6515-6_3
Lu TPalaiahnakote STan CLiu WLu TPalaiahnakote STan CLiu W(2014)Introduction to Video Text DetectionVideo Text Detection10.1007/978-1-4471-6515-6_1(1-18)Online publication date: 30-Jun-2014
https://doi.org/10.1007/978-1-4471-6515-6_1
Sharma NPal UBlumenstein M(2012)Recent Advances in Video Based Document ProcessingProceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems10.1109/DAS.2012.72(63-68)Online publication date: 27-Mar-2012
https://dl.acm.org/doi/10.1109/DAS.2012.72
Kasar TRamakrishnan A(2011)Multi-script and multi-oriented text localization from scene imagesProceedings of the 4th international conference on Camera-Based Document Analysis and Recognition10.1007/978-3-642-29364-1_1(1-14)Online publication date: 22-Sep-2011
https://dl.acm.org/doi/10.1007/978-3-642-29364-1_1

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