Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 |
| Publisher | IEEE Computer Society |
| Pages | 1010-1013 |
| Number of pages | 4 |
| ISBN (Electronic) | 0818671289 |
| DOIs | |
| Publication status | Published - 1995 Jan 1 |
| Event | 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 - Montreal, Canada Duration: 1995 Aug 14 → 1995 Aug 16 |
Publication series
| Name | Proceedings of the International Conference on Document Analysis and Recognition, ICDAR |
|---|---|
| Volume | 2 |
| ISSN (Print) | 1520-5363 |
Conference
| Conference | 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 |
|---|---|
| Country | Canada |
| City | Montreal |
| Period | 95/8/14 → 95/8/16 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network. / Lee, Seong Whan; Kim, Young Joon.
Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995. IEEE Computer Society, 1995. p. 1010-1013 602073 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network
AU - Lee, Seong Whan
AU - Kim, Young Joon
PY - 1995/1/1
Y1 - 1995/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84961717868&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961717868&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.1995.602073
DO - 10.1109/ICDAR.1995.602073
M3 - Conference contribution
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 1010
EP - 1013
BT - Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995
PB - IEEE Computer Society
ER -