Title :
Multiscale handwritten character recognition using CNN image filters
Author :
Saatci, Ertugrul ; Tavsanoglu, Vedat
Author_Institution :
Sch. of Eng., South Bank Univ., London, UK
fDate :
6/24/1905 12:00:00 AM
Abstract :
This paper presents a multi-scale character recognition system consisting of three single-scale recognition systems. The system uses a filter bank of Gabor-type filters implemented by a cellular neural network (CNN). Based on a test set of 26 test characters acting as template and a set consisting of four subsets of 26 unknown handwritten test characters, a maximum 96% and an average 93% correct recognition is provided. This is a considerable improvement over the performance of existing single-scale recognition systems
Keywords :
FIR filters; cellular neural nets; handwritten character recognition; image processing equipment; optical character recognition; performance evaluation; scaling phenomena; Gabor-type filters; cellular neural network; correct recognition performance; filter bank; image filters; multi-scale handwritten character recognition system; single-scale recognition systems; test character template; unknown handwritten test characters; Cellular neural networks; Character recognition; Feature extraction; Feedback; Filter bank; Frequency; Gabor filters; Handwriting recognition; Image recognition; Testing;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007454
