DocumentCode
3083556
Title
A discriminative cascade CNN model for offline handwritten digit recognition
Author
Shulan Pan ; Yanwei Wang ; Changsong Liu ; Xiaoqing Ding
Author_Institution
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
fYear
2015
fDate
18-22 May 2015
Firstpage
501
Lastpage
504
Abstract
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%.
Keywords
convolution; error analysis; handwritten character recognition; learning (artificial intelligence); neural nets; MNIST handwritten digit database; complementary classification objective; convolutional neural network; discriminative cascade CNN model; discriminative learning; error rate; offline handwritten digit recognition; training sample; Distortion; Error analysis; Handwriting recognition; Neural networks; Support vector machines; Training; Writing;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Vision Applications (MVA), 2015 14th IAPR International Conference on
Conference_Location
Tokyo
Type
conf
DOI
10.1109/MVA.2015.7153240
Filename
7153240
Link To Document