Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multimodal fusion using learned text concepts for image categorization
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Text Detection Using Edge Gradient and Graph Spectrum
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
OpenScan: a fully transparent optical scan voting system
EVT/WOTE'10 Proceedings of the 2010 international conference on Electronic voting technology/workshop on trustworthy elections
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Text Localization in Real-World Images Using Efficiently Pruned Exhaustive Search
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
A Keypoint-Based Approach toward Scenery Character Detection
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
Con-text: text detection using background connectivity for fine-grained object classification
Proceedings of the 21st ACM international conference on Multimedia
Automatic Egyptian hieroglyph recognition by retrieving images as texts
Proceedings of the 21st ACM international conference on Multimedia
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We propose to use text recognition to aid in visual object class recognition. To this end we first propose a new algorithm for text detection in natural images. The proposed text detection is based on saliency cues and a context fusion step. The algorithm does not need any parameter tuning and can deal with varying imaging conditions. We evaluate three different tasks: 1. Scene text recognition, where we increase the state-of-the-art by 0.17 on the ICDAR 2003 dataset. 2. Saliency based object recognition, where we outperform other state-of-the-art saliency methods for object recognition on the PASCAL VOC 2011 dataset. 3. Object recognition with the aid of recognized text, where we are the first to report multi-modal results on the IMET set. Results show that text helps for object class recognition if the text is not uniquely coupled to individual object instances.