Automatic Number Plate Recognition (ANPR) System using Machine Learning Techniques
Automatic Number Plate Recognition (ANPR) systems are widely used on a wide range of applications nowadays. The proposed approach has been developed in order to recognise UK number plates from high resolution digital images making use of the latest Computer Vision techniques and Machine Learning met...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/29790 |
| Acceso en línea: | http://hdl.handle.net/10810/29790 |
| Access Level: | acceso abierto |
| Palabra clave: | automatic number plate recognition machine learning artificial intelligence computer vision image processing support vector machine K-nearest neighbour optical character recognition |
| Sumario: | Automatic Number Plate Recognition (ANPR) systems are widely used on a wide range of applications nowadays. The proposed approach has been developed in order to recognise UK number plates from high resolution digital images making use of the latest Computer Vision techniques and Machine Learning methods. For this purpose, a comparison among the different existing Computer Vision techniques used in ANPR is carried out and a deep insight on the operation and mode of use of the most commonly used Machine Learning algorithms in ANPR is provided, being these: Support Vector Machines, Artificial Neural Networks and K-Nearest Neighbours. Besides, for the development of an efficient, fast and reliable ANPR application, a huge car images dataset is created from scratch, necessary both for training the Machine Learning algorithms and for evaluating the performance of the developed system. The global results obtained, which are equal to or above 90% of success with a response time of less than 3 seconds, prove that the system is able to compete with other recently developed ANPR systems of similar characteristics. |
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