Optical character recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Progress in Camera-Based Document Image Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A novel face recognition system using hybrid neural and dual eigenspaces methods
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Camera based Degraded Text Recognition Using Grayscale Feature
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Robust chinese character recognition by selection of binary-based and grayscale-based classifier
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
RII: Renovating the irregular illumination of digital image archives
Journal of Visual Communication and Image Representation
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As the rapid progress of digital imaging technology, the requirements of character recognition for text embedded in image increase dramatically. Many image text characters are in low resolution with heavy degradation. Traditional OCR methods don't have good recognition performance on these degraded images due to poor binarization. In this paper, a novel feature extraction method based on dual eigenspace and synthetic pattern generation is proposed to recognize character images under low resolution. A subpixel grayscale normalization method is first used to normalize the low resolution character images. The dual eigenspace performs classification from coarse to fine. The multi-templates generated from the synthetic patterns provide good robustness against real degradation. Experimental results indicate that our method is very effective on low resolution Japanese character images.