TY - JOUR
T1 - Ring-projection-wavelet-fractal signatures
T2 - a novel approach to feature extraction
AU - Tang, Yuan Y.
AU - Li, Bing F.
AU - Ma, Hong
AU - Liu, Jiming
N1 - Funding Information:
Manuscript received December 18, 1996; revised November 13, 1997. This work was supported by research grants received from Research Grant Council (RGC) of Hong Kong, and Faculty Research Grant (FRG) of Hong Kong Baptist University and by the Ministry of Education of the People’s Republic of China, and Sichuan University, China.
PY - 1998/8
Y1 - 1998/8
N2 - -In this brief, we present a novel approach to optical character recognition that utilizes ring-projection-wavelet-fractal signatures (RPWFS). In particular, the proposed approach reduces the dimensionality of a 2-D pattern by way of a ring-projection method and, thereafter, performs Daubechies' wavelet transformation on the derived 1-D pattern to generate a set of wavelet transformation subpatterns, namely, curves that are nonself-intersecting. Further, from the resulting nonselfintersecting curves, the divider dimensions are readily computed. These divider dimensions constitute a new feature vector for the original 2-D pattern, defined over the curves' fractal dimensions. We have conducted several experiments in which a set of printed alphanumeric symbols of varying fonts and orientation were classified, based on the formulation of our new feature vector. The results obtained from these experiments have consistently shown the character recognition approach with the proposed feature vector can yield an excellent classification rate of 100%. Index Terms-Character recognition, dimensionality reduction, divider dimensions, ring-projection, ring-projection-wavelet-fractal signatures (RPWFS), wavelet transformation.
AB - -In this brief, we present a novel approach to optical character recognition that utilizes ring-projection-wavelet-fractal signatures (RPWFS). In particular, the proposed approach reduces the dimensionality of a 2-D pattern by way of a ring-projection method and, thereafter, performs Daubechies' wavelet transformation on the derived 1-D pattern to generate a set of wavelet transformation subpatterns, namely, curves that are nonself-intersecting. Further, from the resulting nonselfintersecting curves, the divider dimensions are readily computed. These divider dimensions constitute a new feature vector for the original 2-D pattern, defined over the curves' fractal dimensions. We have conducted several experiments in which a set of printed alphanumeric symbols of varying fonts and orientation were classified, based on the formulation of our new feature vector. The results obtained from these experiments have consistently shown the character recognition approach with the proposed feature vector can yield an excellent classification rate of 100%. Index Terms-Character recognition, dimensionality reduction, divider dimensions, ring-projection, ring-projection-wavelet-fractal signatures (RPWFS), wavelet transformation.
UR - http://www.scopus.com/inward/record.url?scp=0032136411&partnerID=8YFLogxK
U2 - 10.1109/82.718824
DO - 10.1109/82.718824
M3 - Journal article
AN - SCOPUS:0032136411
SN - 1057-7130
VL - 45
SP - 1130
EP - 1134
JO - IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing
JF - IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing
IS - 8
ER -