Improved FCM Handwritten Digit Recognition Based on Zer-nike Moments Feature Extraction
Abstract: This paper proposes a method of Zernike moments feature extraction, which is through the improved FCM algorithm for fuzzy clustering of handwritten numerals. Zernike moments of the image have rotational invariance, so the feature space can well reflect the characteristics of the image. Fuzzy C-Means Clustering uses a degree of membership of each data point to determine the degree of belonging to a cluster, and this paper takes weighted fuzzy C-means clustering algorithm to classify the membership. Clustering lies in feature extraction. In the image pre-processing of the premise, this paper reduced the dimension of Zernike moments, and effectively improved the recognition speed without decreasing the recognition rate.
文章引用: 苗春艳 , 杨耀权 , 张 硕 , 韩升晖 (2013) 基于Zernike矩特征提取的改进FCM手写体数字识别。 计算机科学与应用， 3， 180-183. doi: 10.12677/CSA.2013.33031
Copyright © 2020 Hans Publishers Inc. All rights reserved.