JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Systematic Approach for Detecting Text in Images Using Supervised Learning
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Systematic Approach for Detecting Text in Images Using Supervised Learning
Nguyen, Minh Hieu; Lee, GueeSang;
  PDF(new window)
 Abstract
Locating text data in images automatically has been a challenging task. In this approach, we build a three stage system for text detection purpose. This system utilizes tensor voting and Completed Local Binary Pattern (CLBP) to classify text and non-text regions. While tensor voting generates the text line information, which is very useful for localizing candidate text regions, the Nearest Neighbor classifier trained on discriminative features obtained by the CLBP-based operator is used to refine the results. The whole algorithm is implemented in MATLAB and applied to all images of ICDAR 2011 Robust Reading Competition data set. Experiments show the promising performance of this method.
 Keywords
Text localization;Tensor voting;Completed local binary pattern;
 Language
English
 Cited by
 References
1.
Keechul Jung, Kwang In Kim, Anil K.Jain, "Text information extraction in images and video: a survey", Pattern Recognition, vol. 37, no. 5(May 2004), pp. 977-997. crossref(new window)

2.
Victor Wu, Raghavan Manmatha, and Edward M. Riseman, "TextFinder: An Automatic System to Detect and Recognize Text in Images", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 11, November 1999.

3.
G. Medioni, M. S. Lee, and C. K. Tang, "A computational framework for segmentation and grouping", Elsevier, 2000.

4.
Toan Dinh Nguyen, Jonghyun Park, and Gueesang Lee, "Tensor Voting Based Text Localization in Natural Scene Images," IEEE Signal Processing Letters, vol. 17, no. 7, July, 2010, pp. 639-642. crossref(new window)

5.
C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," in International Conference on Computer Vision, 1998, pp. 839-846.

6.
P. Clark and M. Mirmehdi, "Recognising text in real scenes," International Journal on Document Analysis and Recognition, vol. 4, pp. 243-257, 2002. crossref(new window)

7.
Z. Guo, L. Zhang, D. Zhang, "A completed modeling of local binary pattern operator for texture classification", IEEE Transactions on Image Processing 19(2010) 1657-1663. crossref(new window)

8.
Karatzas, S. Robles Mestre, J. Mas, F. Nourbakhsh, P. Pratim Roy, "ICDAR 2011 Robust Reading Competition - Challenge 1: Reading Text in Born-Digital Images (Web and Email)", 11th International Conference of Document Analysis and Recognition, 2011, IEEE CPS, pp. 1485-1490.

9.
C. Wolf and J.M. Jolion, "Object Count / Area Graphs for the Evaluation of Object Detection and Segmentation Algorithms", Int. Journal of Document Analysis, vol. 8, no. 4, pp. 280-296, 2006. crossref(new window)

10.
Deepak Kumar, A. G. Ramakrishnan, "OTCYMIST: Otsu-Canny Minimal Spanning Tree for Born-Digital Images". Document Analysis Systems, 2012, pp.389-393.

11.
C. Yi and Y. Tian, "Text String Detection from Natural Scenes by Structure-based Partition and Grouping", IEEE Transactions on Image Processing, Vol. 20, Issue 9, 2011.

12.
Shehzad Muhammad Hanif, Lionel Prevost, "Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm", ICDAR '09: 10th International Conference on Document Analysis and Recognition, July 2009.