基于试探的未知类别聚类的识别算法应用研究

doi:10.16039/j.cnki.cn22-1249.2021.05.005

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摘要

聚类算法是数字识别的热点研究技术之一，是一种寻找特征相似的聚合类的非监督学习的方法。采用聚类法在vc++6.0环境进行手写体数字识别系统设计。通过图像的分割与特征提取，模式相似性测量和聚类的分析完成识别工作。结果表明基于试探的聚类识别算法的手写数字识别系统识别率达到100%，满足实际应用需求。

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	张飞

关键词: 数字识别特征提取聚类法

Abstract:

Clustering algorithm is one of the hot research technologies in digital recognition. It is a kind of unsupervised learning method to look for clustering classes with similar features. Clustering method is used to design handwritten number recognition system in VC ++6.0 environment. Through image segmentation and feature extraction, pattern similarity measurement and clustering analysis, the recognition work is completed. The results show that the recognition rate of the handwritten number recognition system based on the clustering recognition algorithm can reach 100%, which meets the practical application requirements.

Key words: number recognition feature extraction clustering method

出版日期: 2021-05-25 发布日期: 2021-05-25 整期出版日期: 2021-05-25

TP391

引用本文:

张飞. 基于试探的未知类别聚类的识别算法应用研究 [J]. 吉林化工学院学报, 2021, 38(5): 23-26.
ZHANG Fei. Research on the application of unknown category clustering recognition algorithm based on heuristics . Journal of Jilin Institute of Chemical Technology, 2021, 38(5): 23-26.

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https://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2021.05.005 或 https://xuebao.jlict.edu.cn/CN/Y2021/V38/I5/23

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