Text detection in natural scene images with feature combination

Authors:
Qixiang Ye;Jianbin Jiao;Jun Huang;Hua Yu
Affiliations:
Graduate University of Chinese Academy of Science, Beijing, China;Graduate University of Chinese Academy of Science, Beijing, China;Graduate University of Chinese Academy of Science, Beijing, China;Graduate University of Chinese Academy of Science, Beijing, China
Venue:
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Year:
2007

Citing 10
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
TextFinder: An Automatic System to Detect and Recognize Text In Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Extraction from Web Images Based on A Split-and-Merge Segmentation Method Using Colour Perception

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Text Detection from Natural Scene Images: Towards a System for Visually Impaired Persons

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Reading LCD/LED Displays with a Camera Cell Phone

CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
A Robust Split-and-Merge Text Segmentation Approach for Images

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Fast and robust text detection in images and video frames

Image and Vision Computing
Detecting and reading text in natural scenes

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Automatic text detection and tracking in digital video

IEEE Transactions on Image Processing
Localizing and segmenting text in images and videos

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we proposed a method for text detection in natural scene images by feature combination under a coarse-to-fine framework. Firstly, color feature is used to segment images into color-uniform regions by a clustering algorithm. Then edge features are extracted to construct a weak classifier to classify the regions into candidates or background. After a layout analysis procedure, candidate regions are connected into text lines. Finally texture features, color features, and statistic OCR (Optical Character Reader) features are extracted to discriminate text/non-text with a support vector machine (SVM). Experimental results on a large dataset show that the combination of features in different detection stages is competent for text detection task.