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An Effective Approach for Noise Robust and Rotation Invariant Handwritten Character Recognition Using Zernike Moments Features and Optimal Similarity Measure
Applied Artificial Intelligence ( IF 2.8 ) Pub Date : 2020-08-17 , DOI: 10.1080/08839514.2020.1796370
Chandan Singh 1 , Ashutosh Aggarwal 1, 2
Affiliation  

ABSTRACT 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.

中文翻译:

使用 Zernike Moments 特征和最优相似性度量的噪声鲁棒性和旋转不变手写字符识别的有效方法

摘要 Zernike 矩 (ZM) 是非常有效的正交旋转不变矩。通常,ZM 的大小用作特征描述符,而欧几里得距离用作分类器。最近,已经开发了一些基于 ZM 幅度和相位的分类器,据报道它们在模式匹配问题中非常有效。最近开发的一种相似性度量,称为最佳相似性度量,已知在模式识别问题中,尤其是在噪声条件下,比基于 ZM 幅度的欧几里德距离度量提供了非常好的性能。在本文中,我们研究了传统的基于幅度的相似性度量和新的相似性度量,包括最佳相似性度量,并比较了它们在分段手写字符和数字上的性能。可以看出,最优相似性度量的性能远远优于所有其他相似性度量。即使在非常高的噪声条件下,它的性能也比其他相似性度量好得多。然而,由于其计算所涉及的过程的优化,它很慢。因此,我们还为其计算提出了一种快速算法并降低了其时间复杂度。提供了详细的实验结果以支持上述观察。我们还为其计算提出了一种快速算法并降低了其时间复杂度。提供了详细的实验结果以支持上述观察。我们还为其计算提出了一种快速算法并降低了其时间复杂度。提供了详细的实验结果以支持上述观察。
更新日期:2020-08-17
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