A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Text Detection in Images Based on Unsupervised Classification of Edge-based Features
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Text Localization in Real-World Images Using Efficiently Pruned Exhaustive Search
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Combining image and text features: a hybrid approach to mobile book spine recognition
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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The problem of text detection in natural scene images is challenging because of the unconstrained sizes, colors, backgrounds and alignments of the characters. This paper proposes novel symmetry features for this task. Within a text line, the intra-character symmetry captures the correspondence between the inner contour and the outer contour of a character while the inter-character symmetry helps to extract information from the gap region between two consecutive characters. A formulation based on Gradient Vector Flow is used to detect both types of symmetry points. These points are then grouped into text lines using the consistency in sizes, colors, and stroke and gap thickness. Therefore, unlike most existing methods which use only character features, our method exploits both the text features and the gap features to improve the detection result. Experimentally, our method compares well to the state-of-the-art on public datasets for natural scenes and street-level images, an emerging category of image data. The proposed technique can be used in a wide range of multimedia applications such as content-based image/video retrieval, mobile visual search and sign translation.