A novel hybrid CNN–SVM classifier for recognizing handwritten digits

This paper presents a hybrid model of integrating the synergy of two superior classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM), which have proven results in recognizing different types of patterns. In this model, CNN works as a trainable feature extractor and SVM perf...

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Veröffentlicht in:Pattern recognition Jg. 45; H. 4; S. 1318 - 1325
Hauptverfasser: Niu, Xiao-Xiao, Suen, Ching Y.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.04.2012
Elsevier
Schlagworte:
Applied sciences
Artificial intelligence
Classifiers
Computer science; control theory; systems
Connectionism. Neural networks
Convolutional Neural Network
Digits
Exact sciences and technology
Handwritten digit recognition
Hybrid model
Information, signal and communications theory
Mathematical models
Pattern recognition
Recognition
Rejection
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Support Vector Machine
Support vector machines
Telecommunications and information theory
Convolutional Neural Network
Hybrid model
Support Vector Machine
Handwritten digit recognition
Performance evaluation
Automatic classification
Cellular neural nets
Support vector machine
Pattern recognition
Neural network
Signal classification
Database
Handwritten character recognition
Manuscript character
ISSN:0031-3203, 1873-5142
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This paper presents a hybrid model of integrating the synergy of two superior classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM), which have proven results in recognizing different types of patterns. In this model, CNN works as a trainable feature extractor and SVM performs as a recognizer. This hybrid model automatically extracts features from the raw images and generates the predictions. Experiments have been conducted on the well-known MNIST digit database. Comparisons with other studies on the same database indicate that this fusion has achieved better results: a recognition rate of 99.81% without rejection, and a recognition rate of 94.40% with 5.60% rejection. These performances have been analyzed with reference to those by human subjects. ► We explored a new hybrid of Convolutional Neural Network and Support Vector Machine. ► Experiments were conducted on the MNIST database. ► The hybrid model has achieved better recognition and reliability performances. ► The best recognition rate was 99.81% without rejection. ► A reliability rate of 100% with 5.60% rejection was obtained.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2011.09.021