Optimal sampling intervals for Gabor features and printed Japanese character recognition
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Directional Pattern Matching for Character Recognition Revisited
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using moments features from Gabor directional images for Kannada handwriting character recognition
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Gabor-based recognizer for Chinese handwriting from segmentation-free strategy
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Handwritten Chinese character recognition: effects of shape normalization and feature extraction
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Gabor features for offline Arabic handwriting recognition
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Recognition of Arabic (Indian) bank check digits using log-gabor filters
Applied Intelligence
Texture feature evaluation for segmentation of historical document images
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
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Gabor filter feature has been applied to character recognition but was not compared with the best direction feature: gradient feature. In this paper, we propose a principled method for implementing Gabor filters for character feature extraction and compare the recognition performances of Gabor feature and gradient feature on three databases. The results show that Gabor filters with low orientation sensitivity and broad frequency band favor recognition accuracy. The Gabor feature performs comparably or better than the gradient feature on two of the three databases, but is inferior on the rest one.