A novel hybrid CNN-SVM classifier for recognizing handwritten digits

作者：Niu, Xiao-Xiao^*; Suen, Ching Y.

来源：Pattern Recognition, 2012, 45(4): 1318-1325.

DOI：10.1016/j.patcog.2011.09.021

• Hybrid model
• Convolutional Neural Network
• Support Vector Machine
• Handwritten digit recognition

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.

• 出版日期2012-4