Multifont character recognition by 9×9 DPCNN board

Author

Salermo, M. ; Sargeni, Fausto ; Bonaiuto, Vincenzo ; Favero, Francesco Maria

Author_Institution

Dept. of Electron. Eng., Rome Univ., Italy

Volume

2

fYear

1997

fDate

3-6 Aug. 1997

Firstpage

1338

Abstract

Cellular neural networks are a remarkable artificial neural network class well suited for real time image processing tasks. In fact, the parallel analogue computing feature makes them really effective in such problems which require a real time response. Moreover, the limited amount of interconnections relative to cell´s neighbourhood only, lend themselves to easy VLSI implementation. In previous papers, the authors presented some CNN hardware. Therefore, in this paper, an algorithm for character recognition developed on the 9×9 DPCNN board is presented.

Keywords

VLSI; analogue processing circuits; cellular neural nets; character recognition; image recognition; neural chips; real-time systems; 9×9 DPCNN board; VLSI implementation; artificial neural network class; multifont character recognition; parallel analogue computing feature; real time image processing tasks; Cellular neural networks; Character recognition; Charge coupled devices; Detectors; Image coding; Image segmentation; Iron; Performance evaluation; Pixel; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1997. Proceedings of the 40th Midwest Symposium on

Print_ISBN

0-7803-3694-1

Type

conf

DOI

10.1109/MWSCAS.1997.662329

Filename

662329