CLPSafe: Mobile Application for Avoid Cloned of License Plates Using Deep Learning

Diego Sánchez; John Silva; Cesar Salas

doi:10.1007/978-3-031-63616-5_12

CLPSafe: Mobile Application for Avoid Cloned of License Plates Using Deep Learning

Diego Sánchez
, John Silva
, Cesar Salas

Faculty of Health Sciences

Universidad Peruana de Ciencias Aplicadas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The problem of cloning vehicle license plates in Peru is detailed, by criminals to sell vehicles at a lower price or commit crimes with the stolen vehicle. A mobile application is proposed that uses convolutional neural networks and deeplearning algorithms: TensorFlow, EasyOCR and OpenCV to identify the license plate and its alphanumeric code, obtain detailed information about the vehicle and its owner, and issue reports to the authorities in case of cloned plate or stolen. The objective of the project is to speed up identification, consultation, and issuance of reports regarding vehicular identity theft, thus contributing to improving citizen security missing results. The analyzed results indicate that 75% of the experts expressed favorable opinions regarding the validation of the proposed architecture diagram for CLPSafe. The positive evaluations received endorse the feasibility and effectiveness of the proposed architecture, affirming its potential to effectively tackle the problem of license plate cloning in Peru.

Original language	English
Title of host publication	Information Management and Big Data - 10th Annual International Conference, SIMBig 2023, Proceedings
Editors	Juan Antonio Lossio-Ventura, Eduardo Ceh-Varela, Genoveva Vargas-Solar, Ricardo Marcacini, Claude Tadonki, Hiram Calvo, Hugo Alatrista-Salas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	157-166
Number of pages	10
ISBN (Print)	9783031636158
DOIs	https://doi.org/10.1007/978-3-031-63616-5_12
State	Published - 2024
Event	10th Annual International Conference on Information Management and Big Data, SIMBig 2023 - Mexico City, Mexico Duration: 13 Dec 2023 → 15 Dec 2023

Publication series

Name	Communications in Computer and Information Science
Volume	2142 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	10th Annual International Conference on Information Management and Big Data, SIMBig 2023
Country/Territory	Mexico
City	Mexico City
Period	13/12/23 → 15/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Deep Learning
Mobile Application
Optical Character Recognition (OCR)
vehicle license plate cloning
vehicle license plate fraud

Access to Document

10.1007/978-3-031-63616-5_12

Cite this

Sánchez, D., Silva, J., & Salas, C. (2024). CLPSafe: Mobile Application for Avoid Cloned of License Plates Using Deep Learning. In J. A. Lossio-Ventura, E. Ceh-Varela, G. Vargas-Solar, R. Marcacini, C. Tadonki, H. Calvo, & H. Alatrista-Salas (Eds.), Information Management and Big Data - 10th Annual International Conference, SIMBig 2023, Proceedings (pp. 157-166). (Communications in Computer and Information Science; Vol. 2142 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-63616-5_12

Sánchez, Diego ; Silva, John ; Salas, Cesar. / CLPSafe : Mobile Application for Avoid Cloned of License Plates Using Deep Learning. Information Management and Big Data - 10th Annual International Conference, SIMBig 2023, Proceedings. editor / Juan Antonio Lossio-Ventura ; Eduardo Ceh-Varela ; Genoveva Vargas-Solar ; Ricardo Marcacini ; Claude Tadonki ; Hiram Calvo ; Hugo Alatrista-Salas. Springer Science and Business Media Deutschland GmbH, 2024. pp. 157-166 (Communications in Computer and Information Science).

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title = "CLPSafe: Mobile Application for Avoid Cloned of License Plates Using Deep Learning",

abstract = "The problem of cloning vehicle license plates in Peru is detailed, by criminals to sell vehicles at a lower price or commit crimes with the stolen vehicle. A mobile application is proposed that uses convolutional neural networks and deeplearning algorithms: TensorFlow, EasyOCR and OpenCV to identify the license plate and its alphanumeric code, obtain detailed information about the vehicle and its owner, and issue reports to the authorities in case of cloned plate or stolen. The objective of the project is to speed up identification, consultation, and issuance of reports regarding vehicular identity theft, thus contributing to improving citizen security missing results. The analyzed results indicate that 75\% of the experts expressed favorable opinions regarding the validation of the proposed architecture diagram for CLPSafe. The positive evaluations received endorse the feasibility and effectiveness of the proposed architecture, affirming its potential to effectively tackle the problem of license plate cloning in Peru.",

keywords = "Deep Learning, Mobile Application, Optical Character Recognition (OCR), vehicle license plate cloning, vehicle license plate fraud",

author = "Diego S{\'a}nchez and John Silva and Cesar Salas",

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Sánchez, D, Silva, J & Salas, C 2024, CLPSafe: Mobile Application for Avoid Cloned of License Plates Using Deep Learning. in JA Lossio-Ventura, E Ceh-Varela, G Vargas-Solar, R Marcacini, C Tadonki, H Calvo & H Alatrista-Salas (eds), Information Management and Big Data - 10th Annual International Conference, SIMBig 2023, Proceedings. Communications in Computer and Information Science, vol. 2142 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 157-166, 10th Annual International Conference on Information Management and Big Data, SIMBig 2023, Mexico City, Mexico, 13/12/23. https://doi.org/10.1007/978-3-031-63616-5_12

CLPSafe: Mobile Application for Avoid Cloned of License Plates Using Deep Learning. / Sánchez, Diego; Silva, John; Salas, Cesar.
Information Management and Big Data - 10th Annual International Conference, SIMBig 2023, Proceedings. ed. / Juan Antonio Lossio-Ventura; Eduardo Ceh-Varela; Genoveva Vargas-Solar; Ricardo Marcacini; Claude Tadonki; Hiram Calvo; Hugo Alatrista-Salas. Springer Science and Business Media Deutschland GmbH, 2024. p. 157-166 (Communications in Computer and Information Science; Vol. 2142 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - CLPSafe

T2 - 10th Annual International Conference on Information Management and Big Data, SIMBig 2023

AU - Sánchez, Diego

AU - Silva, John

AU - Salas, Cesar

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - The problem of cloning vehicle license plates in Peru is detailed, by criminals to sell vehicles at a lower price or commit crimes with the stolen vehicle. A mobile application is proposed that uses convolutional neural networks and deeplearning algorithms: TensorFlow, EasyOCR and OpenCV to identify the license plate and its alphanumeric code, obtain detailed information about the vehicle and its owner, and issue reports to the authorities in case of cloned plate or stolen. The objective of the project is to speed up identification, consultation, and issuance of reports regarding vehicular identity theft, thus contributing to improving citizen security missing results. The analyzed results indicate that 75% of the experts expressed favorable opinions regarding the validation of the proposed architecture diagram for CLPSafe. The positive evaluations received endorse the feasibility and effectiveness of the proposed architecture, affirming its potential to effectively tackle the problem of license plate cloning in Peru.

AB - The problem of cloning vehicle license plates in Peru is detailed, by criminals to sell vehicles at a lower price or commit crimes with the stolen vehicle. A mobile application is proposed that uses convolutional neural networks and deeplearning algorithms: TensorFlow, EasyOCR and OpenCV to identify the license plate and its alphanumeric code, obtain detailed information about the vehicle and its owner, and issue reports to the authorities in case of cloned plate or stolen. The objective of the project is to speed up identification, consultation, and issuance of reports regarding vehicular identity theft, thus contributing to improving citizen security missing results. The analyzed results indicate that 75% of the experts expressed favorable opinions regarding the validation of the proposed architecture diagram for CLPSafe. The positive evaluations received endorse the feasibility and effectiveness of the proposed architecture, affirming its potential to effectively tackle the problem of license plate cloning in Peru.

KW - Deep Learning

KW - Mobile Application

KW - Optical Character Recognition (OCR)

KW - vehicle license plate cloning

KW - vehicle license plate fraud

UR - https://www.scopus.com/pages/publications/85199625347

U2 - 10.1007/978-3-031-63616-5_12

DO - 10.1007/978-3-031-63616-5_12

M3 - Contribución a la conferencia

AN - SCOPUS:85199625347

SN - 9783031636158

T3 - Communications in Computer and Information Science

SP - 157

EP - 166

BT - Information Management and Big Data - 10th Annual International Conference, SIMBig 2023, Proceedings

A2 - Lossio-Ventura, Juan Antonio

A2 - Ceh-Varela, Eduardo

A2 - Vargas-Solar, Genoveva

A2 - Marcacini, Ricardo

A2 - Tadonki, Claude

A2 - Calvo, Hiram

A2 - Alatrista-Salas, Hugo

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 13 December 2023 through 15 December 2023

ER -

Sánchez D, Silva J, Salas C. CLPSafe: Mobile Application for Avoid Cloned of License Plates Using Deep Learning. In Lossio-Ventura JA, Ceh-Varela E, Vargas-Solar G, Marcacini R, Tadonki C, Calvo H, Alatrista-Salas H, editors, Information Management and Big Data - 10th Annual International Conference, SIMBig 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 157-166. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-63616-5_12

CLPSafe: Mobile Application for Avoid Cloned of License Plates Using Deep Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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