Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database

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 enou...

Full description

Autores:
Arroyo-Pérez, D E
Álvarez-Canchila, O I
Patiño-Saucedo, A
Rostro González, H
Patiño Vanegas, Alberto
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/9384
Acceso en línea:
https://hdl.handle.net/20.500.12585/9384
Palabra clave:
Character Recognition
Tesseract
Template Matching
Convolution
Database systems
Image processing
License plates (automobile)
Colombians
Large amounts
Vehicle license plates
Convolutional neural networks
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc/4.0/
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dc.title.spa.fl_str_mv Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database
title Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database
spellingShingle Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database
Character Recognition
Tesseract
Template Matching
Convolution
Database systems
Image processing
License plates (automobile)
Colombians
Large amounts
Vehicle license plates
Convolutional neural networks
title_short Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database
title_full Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database
title_fullStr Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database
title_full_unstemmed Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database
title_sort Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k database
dc.creator.fl_str_mv Arroyo-Pérez, D E
Álvarez-Canchila, O I
Patiño-Saucedo, A
Rostro González, H
Patiño Vanegas, Alberto
dc.contributor.author.none.fl_str_mv Arroyo-Pérez, D E
Álvarez-Canchila, O I
Patiño-Saucedo, A
Rostro González, H
Patiño Vanegas, Alberto
dc.subject.keywords.spa.fl_str_mv Character Recognition
Tesseract
Template Matching
Convolution
Database systems
Image processing
License plates (automobile)
Colombians
Large amounts
Vehicle license plates
Convolutional neural networks
topic Character Recognition
Tesseract
Template Matching
Convolution
Database systems
Image processing
License plates (automobile)
Colombians
Large amounts
Vehicle license plates
Convolutional neural networks
description 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.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-09-10T21:24:09Z
dc.date.available.none.fl_str_mv 2020-09-10T21:24:09Z
dc.date.issued.none.fl_str_mv 2020
dc.date.submitted.none.fl_str_mv 2020-09-09
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dc.type.hasversion.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.spa.spa.fl_str_mv Otro
status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv Arroyo-Pérez, D. E., Alvarez-Canchila, O. I., Patĩo-Saucedo, A., Rostro González, H., & Patĩo-Vanegas, A. (2020). Automatic recognition of colombian car license plates using convolutional neural networks and Chars74k database. Paper presented at the Journal of Physics: Conference Series, , 1547(1) doi:10.1088/1742-6596/1547/1/012024
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/9384
dc.identifier.doi.none.fl_str_mv 10.1088/1742-6596/1547/1/012024
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Arroyo-Pérez, D. E., Alvarez-Canchila, O. I., Patĩo-Saucedo, A., Rostro González, H., & Patĩo-Vanegas, A. (2020). Automatic recognition of colombian car license plates using convolutional neural networks and Chars74k database. Paper presented at the Journal of Physics: Conference Series, , 1547(1) doi:10.1088/1742-6596/1547/1/012024
10.1088/1742-6596/1547/1/012024
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/9384
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Atribución-NoComercial 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
Atribución-NoComercial 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 8 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.spatial.none.fl_str_mv Colombia
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv Journal of Physics: Conference Series 1547 (2020) 012024
institution Universidad Tecnológica de Bolívar
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spelling Arroyo-Pérez, D E5af0863a-702e-4e04-aa7e-38bd47293639Álvarez-Canchila, O I4aa2cb07-d3ce-498e-bca2-6501469b0abaPatiño-Saucedo, Ac3a33eee-85f4-42c1-b646-2daa28d3a1cbRostro González, He0f28ea6-3fc7-4ad2-ade9-243a3fb477c4Patiño Vanegas, Alberto1466c16d-4b5e-49f0-8892-894e2ae6e66eColombia2020-09-10T21:24:09Z2020-09-10T21:24:09Z20202020-09-09Arroyo-Pérez, D. E., Alvarez-Canchila, O. I., Patĩo-Saucedo, A., Rostro González, H., & Patĩo-Vanegas, A. (2020). Automatic recognition of colombian car license plates using convolutional neural networks and Chars74k database. Paper presented at the Journal of Physics: Conference Series, , 1547(1) doi:10.1088/1742-6596/1547/1/012024https://hdl.handle.net/20.500.12585/938410.1088/1742-6596/1547/1/012024Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarA 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.8 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference Series 1547 (2020) 012024Automatic recognition of Colombian car license plates using convolutional neural networks and Chars74k databaseinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionOtrohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_8544Character RecognitionTesseractTemplate MatchingConvolutionDatabase systemsImage processingLicense plates (automobile)ColombiansLarge amountsVehicle license platesConvolutional neural networksCartagena de IndiasPúblico generalShivakumara P, Tang D, Asadzadehkaljahi M, Lu T, Pal U and Hossein Anisi M 2018 CAAI Transactions on Intelligence Technology 3(3) 169Halim S, Zulkifli M and Zulkipli M 2019 Journal of Physics: Conference Series 1358(012084) 1Astawa I, Bawa I, Asri S and Kariati N 2019 International Journal of Scientific and Technology Research 8(11) 481Du S, Ibrahim M, Shehata M and Badawy W 2013 IEEE Transactions on Circuits and Systems for Video Technology 23(2) 311Lecun Y, Bengio Y and Hinton G 2015 Nature 521(7553) 436Calderon J, Vargas J and Perez-Ruiz A 2017 License plate recognition for colombian private vehicles based on an embedded system using the zedboard IEEE Colombian Conference on Robotics and Automation (CCRA) (Bogotá: IEEE)De Campos T E, Babu B R, Varma M et al. 2009 Character recognition in natural images Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) vol 2 (Lisbon: INSTICC Press)Mallat S 2016 Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374(2065) 1Schmidhuber J 2015 Neural Networks 61 85Rumelhart D E, Durbin R, Golden R and Chauvin Y 1995 Backpropagation: The basic theory Developments in Connectionist Theory. Backpropagation: Theory, Architectures, and Applications (New Jersey: Lawrence Erlbaum Associates Inc.) pp 1–34Gonzalez R C and Woods R E 2017 Digital Image Processing (New York: Pearson)Patino-Saucedo A, Rostro-Gonzalez H and Conradt J 2018 Tropical fruits classification using an alexnet-type convolutional neural network and image augmentation International Conference on Neural Information Processing (Cambodia: Springer) pp 371–379Bisong E 2019 Google colaboratory Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners (Berkeley: Apress) pp 59–64Wu S, Zhai W and Cao Y 2019 IET Image Processing 13(14) 2744Kessentini Y, Besbes M, Ammar S and Chabbouh A 2019 Expert Systems with Applications 136 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