Fast Vision-Based Object Recognition Using Combined Integral Map
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
On the detection of textual information in metro stations
Proceedings of the 7th International Conference on Frontiers of Information Technology
A method for text localization and recognition in real-world images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
A novel ring radius transform for video character reconstruction
Pattern Recognition
T-HOG: An effective gradient-based descriptor for single line text regions
Pattern Recognition
Learning to discriminate text from synthetic data
Robot Soccer World Cup XV
A framework for improved video text detection and recognition
Multimedia Tools and Applications
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In this paper, we present a robust system to accurately detect and localize texts in natural scene images. For text detection, a region-based method utilizing multiple features and cascade AdaBoost classifier is adopted. For text localization, a window grouping method integrating text line competition analysis is used to generate text lines. Then within each text line, local binarization is used to extract candidate connected components (CCs) and non-text CCs are filtered out by Markov Random Fields (MRF) model, through which text line can be localized accurately. Experiments on the public benchmark ICDAR 2003 Robust Reading and Text Locating Dataset show that our system is comparable to the best existing methods both in accuracy and speed.