TY - JOUR
T1 - Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database
AU - Arroyo-Pérez, D. E.
AU - Alvarez-Canchila, O. I.
AU - Patĩo-Saucedo, A.
AU - Rostro González, H.
AU - Patĩo-Vanegas, A.
N1 - Publisher Copyright:
© 2020 IOP Publishing Ltd. All rights reserved.
PY - 2020/6/18
Y1 - 2020/6/18
N2 - A methodology for the automatic recognition of Colombian car license plates using convolutional neural networks is proposed. One of the biggest challenges when using onvolutional neural network is the demand for large amounts of samples for training. In this work, we show that if we do not have enough images of vehicle license plates to carry out the training, we can complement it with databases of letters and numbers that are not extracted from cars. The network was trained with the Chars74k database and images of characters extracted from plates of Colombian automobiles. The Chars74k contains approximately 74000 images of all the letters of the Spanish alphabet and all digits from 0 to 9. From chars74k database we have chosen 33849, because the Colombian plates have only uppercase letters and digits. Only 3549 (about 10% of the total) images of characters extracted manually from plates of Colombian automobiles were added. At the input of the convolutional neural network, 70% of the images were used for training, 20% for validation and 10% for testing and the resulting validation accuracy was above 99%. By making a preliminary test on Colombian plates never before used in training, a percentage of correctly recognized plates above 98% was achieved.
AB - A methodology for the automatic recognition of Colombian car license plates using convolutional neural networks is proposed. One of the biggest challenges when using onvolutional neural network is the demand for large amounts of samples for training. In this work, we show that if we do not have enough images of vehicle license plates to carry out the training, we can complement it with databases of letters and numbers that are not extracted from cars. The network was trained with the Chars74k database and images of characters extracted from plates of Colombian automobiles. The Chars74k contains approximately 74000 images of all the letters of the Spanish alphabet and all digits from 0 to 9. From chars74k database we have chosen 33849, because the Colombian plates have only uppercase letters and digits. Only 3549 (about 10% of the total) images of characters extracted manually from plates of Colombian automobiles were added. At the input of the convolutional neural network, 70% of the images were used for training, 20% for validation and 10% for testing and the resulting validation accuracy was above 99%. By making a preliminary test on Colombian plates never before used in training, a percentage of correctly recognized plates above 98% was achieved.
UR - http://www.scopus.com/inward/record.url?scp=85087435956&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1547/1/012024
DO - 10.1088/1742-6596/1547/1/012024
M3 - Conference article
AN - SCOPUS:85087435956
SN - 1742-6588
VL - 1547
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012024
T2 - 16th National Meeting on Optics, ENO 2019 and 7th Andean and Caribbean Conference on Optics and Its Applications, CANCOA 2019
Y2 - 26 November 2019 through 30 November 2019
ER -