Character Recognition by Adaptive Statistical Similarity
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Style Consistent Classification of Isogenous Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor wavelet similarity maps for optimising hierarchical road sign classifiers
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent progress on the OCRopus OCR system
Proceedings of the International Workshop on Multilingual OCR
Style modeling for tagging personal photo collections
Proceedings of the ACM International Conference on Image and Video Retrieval
Nearest neighbor based collection OCR
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Underwater live fish recognition using a balance-guaranteed optimized tree
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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A key problem faced by classifier s is coping with styles not represented in the training set. We present an application of hierarchical Bayesian methods to the problem of recognizing degraded printed characters in a variety of fonts. The proposed method works by using training data of various styles and classes to compute prior distributions on the parameters for the class conditional distributions. For classification, the parameters for the actual class conditional distributions are fitted using an EM algorithm. The advantage of hierarchical Bayesian methods is motivated with a theoretical example. Severalfold increases in classification performance relative to style-oblivious and style-conscious are demonstrated on a multifont OCR task.