A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Hybrid Contextural Text Recognition with String Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Fast Lexicon-Based Scene Text Recognition with Sparse Belief Propagation
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An Integrated Algorithm for Text Recognition: Comparison with a Cascaded Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new class of learnable detectors for categorisation
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Automatic detection and recognition of signs from natural scenes
IEEE Transactions on Image Processing
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In this work we propose the use of contextual information provided by web search engine queries for improving text recognition performance. We first describe a framework for automated text recognition from images. It is based on detecting text areas in images by analysis of Maximally Stable Extremal Regions (MSERs) and recognizing characters by simple template matching. The main emphasis of the paper is on introducing a novel method for exploiting contextual information to improve the obtained recognition results. We propose to analyze the results of web search engine queries on two levels of detail (word and sentence level) which both allow to significantly improve the overall text recognition performance. Experimental evaluations on reference data sets prove that dictionary based methods are outperformed and that even based on a low-quality single character recognition method the proposed web search engine extension enables reasonable text recognition results. This work received the ''Best Scientific Paper Award'' at the International Conference on Pattern Recognition (ICPR), 2008 (Donoser et al., 2008).