A novel hybrid CNN–SVM classifier for recognizing handwritten digits - Niu, Xiao-Xiao; Suen, Ching Y. (Pattern recognition, 2012)
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.