Character Recognition Based on PCANet

被引：3

作者：

Liu, Renjun ^{[1
]}

Lu, Tongwei ^{[1
,2
]}

机构：

[1] Wuhan Inst Technol, Sch Comp Sci & Engn, Wuhan, Hubei, Peoples R China

[2] Wuhan Inst Technol, Hubei Key Lab Intelligent Robot, Wuhan, Hubei, Peoples R China

来源：

2016 15TH INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED COMPUTING (ISPDC) | 2016年

关键词：

character recognition; image processing; PCANet;

D O I：

10.1109/ISPDC.2016.60

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The character recognition is an important issue, which has been pursued in recent year. In the paper, we used PCANet(principal component analysis network) to learn the character features. We verified the influence of these parameters on the performance of PCANet by modifying the key parameters of the experiment. Then we made a handwritten dataset to do the experiment and to verify whether the PCANet can also be used to identify. And the results not only was okey but also the recognition rate can reach 95.56%. At last, we compared the experimental results with the experimental results on Caffe.

引用

页码：364 / 367

页数：4

共 8 条

[1]

[Anonymous], 2013, ICML

[2]

[Anonymous], 2009, ICCV

[3]

[Anonymous], CVPR

[4]

[Anonymous], NEURAL COMPUTATION

[5]

[Anonymous], P SPIE INT SOC OPTIC

[6]

Chan T.-H., PCANet: A Simple Deep Learning Baseline for Image Classification?

[7] ImageNet Classification with Deep Convolutional Neural Networks [J].

Krizhevsky, Alex ;

Sutskever, Ilya ;

Hinton, Geoffrey E. .

COMMUNICATIONS OF THE ACM, 2017, 60 (06) :84-90

[8] Gradient-based learning applied to document recognition [J].

Lecun, Y ;

Bottou, L ;

Bengio, Y ;

Haffner, P .

PROCEEDINGS OF THE IEEE, 1998, 86 (11) :2278-2324

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