Jun Sun
ABSTRACT
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.
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Digital Library
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Index Terms
- Low resolution character recognition by dual eigenspace and synthetic degraded patterns
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Reviews
Raida S. K. Al-Alawi
The authors have devised a novel method for the recognition of low-resolution character images, based on the extraction of features from the dual eigenspace and synthetic degraded patterns. The first stage of the recognition system is an image normalization step, where a character is normalized to a 24x24-pixel enhanced grayscale image. A database of synthetic degraded training patterns is generated, based on a simplified video degradation model proposed by Katsuyama et al. [1]. The second stage of the system is based on extracting the dual eigenspace features, which differs from the traditional principal component analysis (PCA) method in the way the individual eigenspace is built. In the dual eigenspace scheme, the individual eigenspace is built for every category, using the first feature of all the training samples, while, in the PCA scheme, it is based on the mean of every category. The proposed system has been applied to the recognition of low-resolution Japanese Kanji characters captured by a digital camera. The performance of the proposed system was compared with the traditional PCA method, and the contour directional method proposed by Shridhar and Kimura [2]. Results shows that the dual eigenspace feature method is much better than the PCA method, however the contour directional method provides better recognition for low-resolution, simply structured characters. The authors present their work in a very clear and systematic way. The different stages of the proposed systems are not original contributions of the authors; however, integrating them might be a new contribution. Therefore, the proposed method has made a significant contribution to the field. Online Computing Reviews Service
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