The nature of statistical learning theory
The nature of statistical learning theory
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Analysis of errors of handwritten digits made by a multitude of classifiers
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A trainable feature extractor for handwritten digit recognition
Pattern Recognition
Deformation Models for Image Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Isolated Handwritten Farsi Numerals Recognition Using Sparse and Over-Complete Representations
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A novel prototype generation technique for handwriting digit recognition
Pattern Recognition
Multimodal biometric system combining ECG and sound signals
Pattern Recognition Letters
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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.