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CLC number: TP391.41

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Received: 2006-06-01

Revision Accepted: 2006-10-10

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.4 P.568~574

http://doi.org/10.1631/jzus.2007.A0568


A learning-based method to detect and segment text from scene images


Author(s):  JIANG Ren-jie, QI Fei-hu, XU Li, WU Guo-rong, ZHU Kai-hua

Affiliation(s):  Department of Computer Science and Technology, Shanghai Jiao Tong University, Shanghai 200240, China

Corresponding email(s):   blizard1982@sjtu.edu.cn

Key Words:  Text detection, Text segmentation, Text feature, Attentional cascade


JIANG Ren-jie, QI Fei-hu, XU Li, WU Guo-rong, ZHU Kai-hua. A learning-based method to detect and segment text from scene images[J]. Journal of Zhejiang University Science A, 2007, 8(4): 568~574.

@article{title="A learning-based method to detect and segment text from scene images",
author="JIANG Ren-jie, QI Fei-hu, XU Li, WU Guo-rong, ZHU Kai-hua",
journal="Journal of Zhejiang University Science A",
volume="8",
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pages="568~574",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0568"
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%T A learning-based method to detect and segment text from scene images
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%A QI Fei-hu
%A XU Li
%A WU Guo-rong
%A ZHU Kai-hua
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 4
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%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0568

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T1 - A learning-based method to detect and segment text from scene images
A1 - JIANG Ren-jie
A1 - QI Fei-hu
A1 - XU Li
A1 - WU Guo-rong
A1 - ZHU Kai-hua
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 4
SP - 568
EP - 574
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A0568


Abstract: 
This paper proposes a learning-based method for text detection and text segmentation in natural scene images. First, the input image is decomposed into multiple connected-components (CCs) by Niblack clustering algorithm. Then all the CCs including text CCs and non-text CCs are verified on their text features by a 2-stage classification module, where most non-text CCs are discarded by an attentional cascade classifier and remaining CCs are further verified by an SVM. All the accepted CCs are output to result in text only binary image. Experiments with many images in different scenes showed satisfactory performance of our proposed method.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[6] Kim, K.C., Byun, H.R., Song, Y.J., Choi, Y.W., Chi, S.Y., Kim, K.K., Chung, Y.K., 2004. Scene Text Extraction in Natural Scene Images Using Hierarchical Feature Combining and Verification. Proc. International Conference on Computer Vision and Pattern Recognition, 2:679-682.

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