A novel hybrid CNN-SVM classifier for recognizing handwritten digits

Pattern Recognition

Volumn 45, Issue 4, 2012, Pages 1318-1325

  Niu, Xiao Xiao ^a   Suen, Ching Y ^a  

^a Gina Cody School of Engineering and Computer Science   (Canada)

Author keywords

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

Indexed keywords

CONVOLUTIONAL NEURAL NETWORK; FEATURE EXTRACTOR; HANDWRITTEN DIGIT; HANDWRITTEN DIGIT RECOGNITION; HUMAN SUBJECTS; HYBRID MODEL; RAW IMAGES; RECOGNITION RATES;

CONVOLUTION; NEURAL NETWORKS; SUPPORT VECTOR MACHINES;

CHARACTER RECOGNITION;

EID: 83655163714     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2011.09.021     Document Type: Article

References (20)

1. D.C. Ciresan, U. Meier, L.M. Gambardella, J. Schmidhuber, Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition, CoRR, abs/1003.0358, 2010.
2. M. Szarvas, A. Yoshizawa, M. Yamamoto, J. Ogata, Pedestrian detection with convolutional neural networks, in: Proceedings of the IEEE on Intelligent Vehicles Symposium, June 2005, pp. 224229.
3. K. Mori, M. Matsugu, T. Suzuki, Face recognition using SVM fed with intermediate output of CNN for face detection, in: Proceedings of the IAPR Conference on Machine Vision Applications, Tsukuba Science City, Japan, May 2005, pp. 410413.
4. F. Lauer, C.Y. Suen, and G. Bloch A trainable feature extractor for handwritten digit recognition Pattern Recognition 40 6 June 2007 1816 1824 (Pubitemid 46402919)
5. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer New York
6. C.W. Hsu, and C.J. Lin A comparison of methods for multi-class support vector machines IEEE Transactions on Neural Networks 13 2 2002 415 425
7. C.C. Chang, C.J. Lin, LIBSVM: A Library for Support Vector Machines, Software Available at 〈http://www.csie.ntu.edu.tw/∼cjlin/libsvm〉, 2001.
8. T.F. Wu, C.J. Lin, and R.C. Weng Probability estimates for multi-class classification by pairwise coupling Journal of Machine Learning Research 5 2004 975 1005
9. T. Hastie, and R. Tibshirani Classification by pairwise coupling The Annals of Statistics 26 1 1988 451 471
10. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner Gradient-based learning applied to document recognition Proceedings of the IEEE 86 11 1998 2278 2324
11. W. Pan, T.D. Bui, C.Y. Suen, Isolated handwritten Farsi numerals recognition using sparse and over-complete representations, in: Proceedings of the International Conference on Document Analysis and Recognition, Barcelona, Spain, July 2009, pp. 586590.
12. M. Ranzato, C. Poultney, S. Chopra, and Y. LeCun Efficient learning of sparse representations with an energy-based model John Platt Bernhard Schölkopf, Thomas Hofmann, Advances in Neural Information Processing Systems 2006 MIT Press 1134 1144
13. P.Y. Simard, D. Steinkraus, J.C. Platt, Best practice for convolutional neural networks applied to visual document analysis, in: Proceedings of the International Conference on Document Analysis and Recognition, Edinburgh, Scotland, 2, 2003, pp. 958962.
14. The MNIST Database of Handwritten Digits, 〈http://yann.lecun.com/ exdb/mnist/〉.
15. P. Simard, Y.L. Cun, and J. Denker Efficient pattern recognition using a new transformation distance Advances in Neural Information Processing Systems 5 1993 50 58
16. Y. Mizukami, K. Yamaguchi, J. Warrell, P. Li, S. Prince, CUDA implementation of deformable pattern recognition and its application to MNIST handwritten digit database, in: Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey, 2010, pp. 20002004.
17. D. Keysers, T. Deselaers, C. Gollan, and H. Ney Deformation models for image recognition IEEE Transactions on Pattern Analysis and Machine Intelligence 29 8 2007 1422 1435 August (Pubitemid 47040440)
18. H. Cecotti, A. Belaid, Rejection strategy for convolutional neural network for adaptive topology applied to handwritten digits recognition, in: Proceedings of International Conference on Document Analysis and Recognition, Seoul, Korea, 2005, pp. 765769.
19. J.X. Dong, HeroSvm 2.1, 〈http://www.cenparmi.concordia.ca/ ∼jdong/HeroSvm.html〉.
20. C.Y. Suen, and J.N. Tan Analysis of errors on handwritten digits made by a multitude of classifiers Pattern Recognition Letters 26 2005 369 379 (Pubitemid 40120818)