Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Character Recognition Systems: A Guide for Students and Practitioners
Character Recognition Systems: A Guide for Students and Practitioners
Image analysis by discrete orthogonal Racah moments
Signal Processing
Image analysis by discrete orthogonal dual Hahn moments
Pattern Recognition Letters
Moments and Moment Invariants in Pattern Recognition
Moments and Moment Invariants in Pattern Recognition
Image analysis by discrete orthogonal hahn moments
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
Multimedia Tools and Applications
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The selection of a good feature extraction technique is very important in any classification problem. Moments, especially orthogonal moments, seem to be a powerful option in the case of digital image compression, description and recognition. Nowadays, there is a considerable amount of orthogonal moments reported in the literature, each one with some advantages and drawbacks. In this paper, we carry out an experimental comparison of several orthogonal moments for the character recognition problem. Firstly, we compare orthogonal moments with other kinds of feature extraction methods and after that, we compare the different orthogonal moments taking into account different evaluation parameters. Experiments were made by using printed and handwritten digit datasets and the well-known measures: precision, recall and accuracy were used to validate the results. This experimental study corroborates the good performance of orthogonal moments. Besides, more specific results obtained in different kinds of experimentations allow coming to conclusions that could be very useful for the community of image recognition practitioners.