On the detection of textual information in metro stations
Proceedings of the 7th International Conference on Frontiers of Information Technology
New approach based on texture and geometric features for text detection
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Text localization in natural scene images by mean-shift clustering and parallel edge feature
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
T-HOG: An effective gradient-based descriptor for single line text regions
Pattern Recognition
A text reading algorithm for natural images
Image and Vision Computing
Text extraction from natural scene image: A survey
Neurocomputing
A framework for improved video text detection and recognition
Multimedia Tools and Applications
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We have proposed a complete system for text detection and localization in gray scale scene images. A boosting framework integrating feature and weak classifier selection based on computational complexity is proposed to construct efficient text detectors. The proposed scheme uses a small set of heterogeneous features which are spatially combined to build a large set of features. A neural network based localizer learns necessary rules for localization. The evaluation is done on the challenging ICDAR 2003 robust reading and text locating database. The results are encouraging and our system can localize text of various font sizes and styles in complex background.