In this paper we describe an experimental system for the recognition of Italian-style car license plates. Images are usually taken from a camera at a toll gate and preprocessed by a fast and robust 1-D DFT scheme to find the plate and character positions. Characters are classified by a multilayer neural network trained by the recently developed BRLS learning algorithm. The same neural network replaces both the traditional feature extractor and the classifier. The percentage of correctly recognized characters reaches the best scores obtained in literature, being highly insensitive to the environment variability, while the architecture appears best suited for parallel implementation on programmable DSP processors.

Car plate recognition by neural networks and image processing / Parisi, Raffaele; DI CLAUDIO, Elio; Lucarelli, G.; Orlandi, Gianni. - STAMPA. - 3:(1998), pp. 195-198. (Intervento presentato al convegno ISCAS 1998 tenutosi a Monterey, CA, USA nel 31 May-03 Jun 1998) [10.1109/ISCAS.1998.703970].

Car plate recognition by neural networks and image processing

PARISI, Raffaele;DI CLAUDIO, Elio;ORLANDI, Gianni
1998

Abstract

In this paper we describe an experimental system for the recognition of Italian-style car license plates. Images are usually taken from a camera at a toll gate and preprocessed by a fast and robust 1-D DFT scheme to find the plate and character positions. Characters are classified by a multilayer neural network trained by the recently developed BRLS learning algorithm. The same neural network replaces both the traditional feature extractor and the classifier. The percentage of correctly recognized characters reaches the best scores obtained in literature, being highly insensitive to the environment variability, while the architecture appears best suited for parallel implementation on programmable DSP processors.
1998
0780344553
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/243329
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