Skripsi

PENGENALAN PLAT NOMOR KENDARAAN MENGGUNAKAN DEEP LEARNING BERDASARKAN CITRA BERGERAK PADA JALAN TOL.

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Penilaian

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Vehicle License Plate Pattern Recognition is a critical component in traffic monitoring systems, parking security, and various other applications. In this research, we propose a vehicle license plate pattern recognition system that combines YOLOv7 (You Only Look Once version 7) as an object detector and TesseractOCR as a text character recognizer. Under these conditions, researchers developed software to recognize vehicle license plate patterns from moving images using the YOLOv7 method and TesseractOCR to identify characters on the license plate, making it easier for officers to recognize vehicle plates. TesseractOCR, a powerful Optical Character Recognition (OCR) engine, is used to recognize text characters on the license plates. TesseractOCR has the capability to recognize various font styles and languages, making it an ideal choice for character recognition on diverse vehicle plates. The testing results of the proposed system showed good accuracy, even in complex situations. The software was builtusing four combinations of Epoch and batch size, namely Batch 16 Epoch 50, Batch 16 Epoch 100, Batch 16 Epoch 250, and Batch 16 Epoch 500 to obtain trained models for the testing process. The testing process was carried out directly using moving images. The accuracy achieved was 80%, with a precision of 87.5%and a recall of 90.3%. This system has significant potential for use in various applications such as traffic monitoring and vehicle security

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307006634	T130932	T1309322023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1309322023

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

vi, 78 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Jaringan Komunikasi Komputer
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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