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

共 50 条

[31] Neocognitron for handwritten digit recognition
Fukushima, K
NEUROCOMPUTING, 2003, 51 : 161 - 180
[32] A Fine-Tuned Hybrid Stacked CNN to Improve Bengali Handwritten Digit Recognition
Amin, Ruhul
Reza, Md. Shamim
Okuyama, Yuichi
Tomioka, Yoichi
Shin, Jungpil
ELECTRONICS, 2023, 12 (15)
[33] A high-performance CNN method for offline handwritten Chinese character recognition and visualization
Pavlo Melnyk
Zhiqiang You
Keqin Li
Soft Computing, 2020, 24 : 7977 - 7987
[34] OFFLINE KURDISH CHARACTER HANDWRITTEN RECOGNITION (OKCHR) USING CNN WITH VARIOUS PREPROCESSING TECHNIQUES
Ibrahim, Reem M.
Nariman, Goran Saman
Majeed, Hamsa D.
JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2023, 18 (06): : 3113 - 3127
[35] Offline Handwritten New Tai Lue Characters Recognition Using CNN-SVM
Wang, Yongqiang
Yu, Pengfei
Li, Chao
PROCEEDINGS OF 2019 IEEE 2ND INTERNATIONAL CONFERENCE ON ELECTRONIC INFORMATION AND COMMUNICATION TECHNOLOGY (ICEICT 2019), 2019, : 636 - 639
[36] A high-performance CNN method for offline handwritten Chinese character recognition and visualization
Melnyk, Pavlo
You, Zhiqiang
Li, Keqin
SOFT COMPUTING, 2020, 24 (11) : 7977 - 7987
[37] Discriminative structure selection method of Gaussian Mixture Models with its application to handwritten digit recognition
Chen, Xuefeng
Liu, Xiabi
Jia, Yunde
NEUROCOMPUTING, 2011, 74 (06) : 954 - 961
[38] Discriminative Features Mining for Offline Handwritten Signature Verification
Neamah, Karrar
Mohamad, Dzulkifli
Saba, Tanzila
Rehman, Amjad
3D RESEARCH, 2014, 5 (01): : 1 - 6
[39] Handwritten digit recognition by neural 'gas' model and population decoding
Zhang, BL
Fu, MY
Yan, H
IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, 1998, : 1727 - 1731
[40] Model-based on-line handwritten digit recognition
Li, XL
Plamondon, R
Parizeau, M
FOURTEENTH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1 AND 2, 1998, : 1134 - 1136

← 1 2 3 4 5 →