In this paper, we propose a new scheme for multiresolution recognition of totally unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: a feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying totally unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, that of Electro-Technical Laboratory of Japan, and that of Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes.
Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network
Proceedings of 3rd International Conference on Document Analysis and Recognition ; 2 ; 1010-1013 vol.2
1995-01-01
372512 byte
Conference paper
Electronic Resource
English
British Library Conference Proceedings | 1995
|A Majority Voting Scheme for Multiresolution Recognition of Handprinted Numerals
British Library Conference Proceedings | 2003
|Evaluation of Codes and Primitives: Recognition of Unconstrained Handwritten Numerals
British Library Conference Proceedings | 1995
|