@inproceedings{b0ac9265477c4f39882d02563e4b0094,
title = "Recognition Effects of Deep Convolutional Neural Network on Smudged Handwritten Digits",
abstract = "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.",
keywords = "Deep convolutional neural network, MNIST dataset, handwritten digits discrimination, noise",
author = "Zhe Xu and Yusuke Terada and Dongbao Jia and Zonghui Cai and Shangce Gao",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 5th International Conference on Information Science and Control Engineering, ICISCE 2018 ; Conference date: 20-07-2018 Through 22-07-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICISCE.2018.00093",
language = "英語",
series = "Proceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "412--416",
editor = "Shaozi Li and Ying Dai and Yun Cheng",
booktitle = "Proceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018",
}