TY  - JOUR
T1  - Recognition of Contour Invariants with Neurofuzzy Classifier
AU - , Siti Mariyam Shamsuddin AU - , Azah Kamilah Muda AU - , Tan Shuen Chuan 
JO  - Asian Journal of Information Technology
VL  - 5
IS  - 9
SP  - 924
EP  - 932
PY  - 2006
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2006.924.932
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2006.924.932
KW  - Contour invariants
KW  -geometric invariants
KW  -zernike invariants
KW  -neurofuzzy
AB  - In this study, we explore contour invariants for handwritten digits recognition with neuro-fuzzy classifier. We use fuzzy triangular function in backpropagation network to initialize the weights. The results reveal that fuzzy triangular membership function manages to decrease the network convergence rate with proper parameter setting. In this study, unthinned images are appropriate for training and classification purpose as it preserves the images’ significant features. From our experiments, the results show that contour invariants exhibits highest rate of classification compares to geometric and Zernike invariants.
ER  -