Automatic system for localization and recognition of vehicle plate numbers
This paper proposes a vehicle numbers plate identification system, which extracts the characters features of a plate from a captured image by a digital camera. Then identify the symbols of the number plate using a multilayer neural network. The proposed recognition system consists of two processes:...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2003 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Journal of Applied Research and Technology |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs2.localhost:article/610 |
| Acceso en línea: | https://jart.icat.unam.mx/index.php/jart/article/view/610 |
| Access Level: | acceso abierto |
| Palabra clave: | Alphabet recognition Number plate Identification with neural networks |
| Sumario: | This paper proposes a vehicle numbers plate identification system, which extracts the characters features of a plate from a captured image by a digital camera. Then identify the symbols of the number plate using a multilayer neural network. The proposed recognition system consists of two processes: The training process and the recognition process. During the training process, a database is created using 310 vehicular plate images. Then using this database a multilayer neural network is trained to identify the symbols in the vehicles plate. While the recognition process consists of four stages: The number plate localization stage, the binarization stage, the segmentation stage and the ecognition stage which uses the previously trained multilayer neural network. The performance of proposed system is evaluated using more than 1200 symbols from the 310 captured images. The simulation results show that approximately 91.5% of the 310 plate images in the vehicle have been correctly located. The proposed system performance, regarding the identification of numbers and letters in the plate, was evaluated separately. Here the recognition rate is 95.55% and 91.6%, respectively. So the global recognition rate of the vehicle number plate becomes approximately 91.2%. Then from the simulation results it follows that the proposed system works fairly well and then it may be applied in the solution of several practical problems that require automatic number plate identification. |
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