Recognition Effects of Deep Convolutional Neural Network on Smudged Handwritten Digits

被引：0
|
作者：
Xu, Zhe [1 ,2 ]
Terada, Yusuke [2 ]
Jia, Dongbao [2 ,3 ]
Cai, Zonghui [2 ]
Gao, Shangce [2 ]
机构：
[1] Changzhou Inst Technol, Sch Comp Informat & Engn, Changzhou 213032, Jiangsu, Peoples R China
[2] Huaihai Inst Technol, Sch Comp Engn, Lianyugang 222005, Peoples R China
[3] Univ Toyama, Fac Engn, Toyama 9308555, Japan
来源：
2018 5TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2018) | 2018年
关键词：
Deep convolutional neural network; handwritten digits discrimination; MNIST dataset; noise; MODEL;
D O I：
10.1109/ICISCE.2018.00093
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Deep convolutional neural network (CNN) is known to be the first truly successful deep learning approach for image processing and understanding, e.g., the handwritten digits discrimination. However, in real applications such as handwritten zip code recognition, the collected images are commonly with smudged background. In this paper, we study the recognition effects of CNN on smudged digits and compared the results with three-layered perceptron. Experimental results based on MNIST dataset with smudged background (simulated by salt-and-pepper and gaussian noises) show that a drastic decline of recognition accuracy is observed for CNN, suggesting that the extracted features by convolutional operation and max pooling is very sensitive to the noise.
引用
收藏
页码：412 / 416
页数：5
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