TY - GEN
T1 - An EMD-based recognition approach for similar handwritten numerals
AU - Li, He-Long
AU - Kwong, Sam
AU - Li, Yan-Xiong
PY - 2009
Y1 - 2009
N2 - This paper presents a novel approach to recognize similar handwritten numerals based on empirical mode decomposition (EMD), We firstly use the local maximum modulus of wavelet transform (MMWT) to get the width-invariant and grey-level invariant characterization of contours in an image. Then we apply EMD analysis to decompose the synthetic shift normalization of curvature into their components, which could produce more compact features. Finally, three different classifiers, i.e. support vector machine (SVM), hidden Markov model (HMM), and artificial neural network (ANN), are used to discriminate similar handwritten numerals for testing the effectiveness of the extracted features. Experimental results show that the proposed approach obtains higher recognition rates compared with the traditional algorithm for extracting features. © 2009 IEEE.
AB - This paper presents a novel approach to recognize similar handwritten numerals based on empirical mode decomposition (EMD), We firstly use the local maximum modulus of wavelet transform (MMWT) to get the width-invariant and grey-level invariant characterization of contours in an image. Then we apply EMD analysis to decompose the synthetic shift normalization of curvature into their components, which could produce more compact features. Finally, three different classifiers, i.e. support vector machine (SVM), hidden Markov model (HMM), and artificial neural network (ANN), are used to discriminate similar handwritten numerals for testing the effectiveness of the extracted features. Experimental results show that the proposed approach obtains higher recognition rates compared with the traditional algorithm for extracting features. © 2009 IEEE.
KW - Discrimination of handwritten numerals
KW - Empirical mode decomposition (EMD)
KW - Feature extraction
KW - Hilbert-Huang transform (HHT)
UR - https://www.scopus.com/pages/publications/70449338686
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-70449338686&origin=recordpage
U2 - 10.1109/ICMLC.2009.5212792
DO - 10.1109/ICMLC.2009.5212792
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781424437030
VL - 6
SP - 3600
EP - 3605
BT - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
T2 - 2009 International Conference on Machine Learning and Cybernetics
Y2 - 12 July 2009 through 15 July 2009
ER -