An Effective Approach for Noise Robust and Rotation Invariant Handwritten Character Recognition Using Zernike Moments Features and Optimal Similarity Measure

Zernike moments (ZMs) are very effective orthogonal rotation invariant moments. Conventionally, the magnitudes of ZMs are used as feature descriptors and the Euclidean distance is used as a classifier. Recently, a few classifiers based on ZM magnitude and phase have been developed which are reported to be very effective in pattern matching problems. One such a recently developed similarity measure, known as optimal similarity measure, is known to provide very good performance over the ZM magnitude-based Euclidean distance measure in pattern recognition problems, especially under noisy conditions. In this paper, we investigate the conventional magnitude-based similarity measure and the new similarity measures including the optimal similarity measure and compare their performance on segmented handwritten characters and numerals. It is observed that the performance of optimal similarity measure is far better than all other similarity measures. Its performance is very much better than other similarity measures even under very high noisy condition. However, it is slow owing to the optimization of the process involved in its computation. Therefore, we also propose a fast algorithm for its computation and reduce its time complexity. Detailed experimental results are provided to support the above observations.