Automatic recognition and license plate detection model based on OpenCV and Machine Learning
Automatic number plate recognition (ALPR) is an important task with many applications in intelligent transportation and surveillance systems. This research takes into consideration the functions of image processing for the detection and recognition of number plates; which can come from noisy sources...
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| Formato: | Ponencia |
| Lenguaje: | inglés |
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Institute of Electrical and Electronics Engineers Inc.
2022
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| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| _version_ | 1881508895151620096 |
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| author | Ccoto Huallpa, Elias Sullon Macalupu, Abel Angel Otazu Luque, Jorge Eddy Sanchez Garces, Jorge Alejandro |
| author_browse | Ccoto Huallpa, Elias Otazu Luque, Jorge Eddy Sanchez Garces, Jorge Alejandro Sullon Macalupu, Abel Angel |
| author_facet | Ccoto Huallpa, Elias Sullon Macalupu, Abel Angel Otazu Luque, Jorge Eddy Sanchez Garces, Jorge Alejandro |
| author_sort | Ccoto Huallpa, Elias |
| collection | Producción científica |
| description | Automatic number plate recognition (ALPR) is an important task with many applications in intelligent transportation and surveillance systems. This research takes into consideration the functions of image processing for the detection and recognition of number plates; which can come from noisy sources, low illumination, different angles and distances taken from the images (uncontrolled environments); most of the existing automated number plate recognition systems only work in a controlled environment where images are captured from a right angle with good illumination and clarity [25]. According to [11] in uncontrolled environments the probability of recognising the characters on the plates decreases and this has been observed in the research. To achieve image processing, morphological transformation, Gaussian smoothing and Gaussian thresholding were used, then 3 different algorithms K-NN, SVM and Tesseract were used for character recognition, each algorithm with their respective hyperparameters for optimisation. The images were separated into two groups, the first with 80 images taken from different angles and distance (uncontrolled environment) where the best Overall accuracy was obtained with 86 % and the second group were images taken at a right angle and similar distance (controlled environment), this group obtained an Overall accuracy of 95.5 %. |
| format | Conference paper |
| id | oai:cris.upeu.edu.pe:20.500.15037/3132 |
| institution | UPeU |
| language | English |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| publisherStr | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/31322026-11-02T22:00:04ZAutomatic recognition and license plate detection model based on OpenCV and Machine LearningModelo de reconocimiento automático y detección de matrículas basado en OpenCV y Machine LearningCcoto Huallpa, EliasSullon Macalupu, Abel AngelOtazu Luque, Jorge EddySanchez Garces, Jorge AlejandroAutomatic number plate recognition (ALPR) is an important task with many applications in intelligent transportation and surveillance systems. This research takes into consideration the functions of image processing for the detection and recognition of number plates; which can come from noisy sources, low illumination, different angles and distances taken from the images (uncontrolled environments); most of the existing automated number plate recognition systems only work in a controlled environment where images are captured from a right angle with good illumination and clarity [25]. According to [11] in uncontrolled environments the probability of recognising the characters on the plates decreases and this has been observed in the research. To achieve image processing, morphological transformation, Gaussian smoothing and Gaussian thresholding were used, then 3 different algorithms K-NN, SVM and Tesseract were used for character recognition, each algorithm with their respective hyperparameters for optimisation. The images were separated into two groups, the first with 80 images taken from different angles and distance (uncontrolled environment) where the best Overall accuracy was obtained with 86 % and the second group were images taken at a right angle and similar distance (controlled environment), this group obtained an Overall accuracy of 95.5 %.Conference paper10.1109/CIMPS57786.2022.100356872-s2.0-85148697442https://hdl.handle.net/20.500.15037/3132enInternational Conference on Software Process Improvement (CIMPS)Applications in Software Engineering - Proceedings of the 11th International Conference on Software Process Improvement, CIMPS 20229798350398960Institute of Electrical and Electronics Engineers Inc.2022-10-192026-06-27T02:36:02Z2026-06-27T02:36:02Z |
| spellingShingle | Ccoto Huallpa, Elias Sullon Macalupu, Abel Angel Otazu Luque, Jorge Eddy Sanchez Garces, Jorge Alejandro Automatic recognition and license plate detection model based on OpenCV and Machine Learning |
| title | Automatic recognition and license plate detection model based on OpenCV and Machine Learning |
| title_full | Automatic recognition and license plate detection model based on OpenCV and Machine Learning |
| title_fullStr | Automatic recognition and license plate detection model based on OpenCV and Machine Learning |
| title_full_unstemmed | Automatic recognition and license plate detection model based on OpenCV and Machine Learning |
| title_short | Automatic recognition and license plate detection model based on OpenCV and Machine Learning |
| title_sort | automatic recognition and license plate detection model based on opencv and machine learning |
| url | https://hdl.handle.net/20.500.15037/3132 |
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