A Discriminative Cascade CNN Model for Offline Handwritten Digit Recognition

被引：0

作者：

Pan, Shulan ^{[1
]}

Wang, Yanwei

Liu, Changsong

Ding, Xiaoqing

机构：

[1] Tsinghua Univ, Dept Elect Engn, State Key Lab Intelligent Technol, Beijing, Peoples R China

来源：

2015 14TH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS (MVA) | 2015年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a high-performance two-stage cascade CNN model. The main idea behind the cascade CNN model is complementary classification objectives between Stage I and Stage II. Discriminative learning is introduced to train Stage II by feeding back poorly recognized training samples. Experiments have been conducted on the competitive MNIST handwritten digit database. The cascade model achieved the best state-of-the-art performance with an error rate of 0.18%.

引用

页码：501 / 504

页数：4

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