Vehicle images reconstruction using SRCNN for improving the recognition accuracy of vehicle license plate number

Swastika, Windra; Sakti, Ekky Rino Fajar; Subianto, Mochamad

doi:10.14710/jtsiskom.2020.13726

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…This past research not only underscores the reliability of Tesseract in the character recognition phase but also highlights its adaptability in handling diverse image conditions, contributing valuable insights to the field of license plate detection and recognition. Moreover, the study conducted by W. Swastika et al [15] explores the enhancement of recognition accuracy for vehicle license plate numbers through Vehicle Image Reconstruction using a Super-resolution Convolutional Neural Network (SRCNN). This research employs two recognition methods, Tesseract and Stereography Projection Network (SPNet).…”

Section: B License Plate Recognitionmentioning

confidence: 99%

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YOLOv8 and Faster R-CNN Performance Evaluation with Super-resolution in License Plate Recognition

Angelika Mulia,

Safitri,

Gede Putra Kusuma Negara

2024

IJCDS

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License Plate Recognition (LPR) systems are now indispensable technology for law enforcement, border control, traffic management, and parking facilities, among other sectors. They enable enhancement in security and public safety, streamline traffic management, boost staff productivity, and deliver a seamless experience for customers. However, the current model faces challenges in producing high-quality images from a fixed-angle camera to produce accurate results in recognizing characters on the license plate. Weather conditions and unfavorable LP angles can lead to low-resolution images, causing inaccuracies in recognizing the character. Therefore, improvement is needed. In the past, to solve these issues, researchers have developed Super-resolution (SR) models capable of generating high-resolution images from low-resolution counterparts. In this paper, the authors enhance the LPR technology to become an automatic solution which is called Automatic License Plate Recognition (ALPR) system by incorporating SR, aiming to automatically improve character recognition. The study comprises two phases: the detection phase and the recognition phase. In the detection phase, the authors utilize state-of-the-art object detection models, including the YOLOv8 model, and the Faster R-CNN model that uses detectron2. These models perform well. YOLOv8 achieves 93% accuracy in both train and validation datasets, and 90% in the test dataset. While Faster R-CNN achieves 71%, and 74%, respectively. In the recognition phase, the authors employ stand-alone Tesseract-OCR and SRGAN-enabled Tesseract-OCR. The end-to-end pipeline achieves a Character Error Rate (CER) of 53.9% (stand-alone Tesseract-OCR) and 51.7% (SRGAN-enabled Tesseract-OCR ). At the same time, Levenshtein distance achieves 3.6% (stand-alone Tesseract-OCR) and 3.5% (SRGAN-enabled Tesseract-OCR). This highlights the effectiveness of SRGAN in enhancing image quality and, consequently, improving the performance of OCR engines. The insights gained from this study can contribute to the development of robust license plate recognition systems for real-world deployment.

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Section: B License Plate Recognitionmentioning

confidence: 99%

“…Subsequently, the study conducted by W. Swastika et al [15], has limitations because of using a Superresolution Convolutional Neural Network (SRCNN) which is a conventional technique. However, it uses two methods, Tesseract and Stereography Projection Network (SPNet).…”

Section: Super-resolution (Sr)mentioning

confidence: 99%

YOLOv8 and Faster R-CNN Performance Evaluation with Super-resolution in License Plate Recognition

Angelika Mulia,

Safitri,

Gede Putra Kusuma Negara

2024

IJCDS

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“…Penelitian yang telah dilakukan oleh Swastika windra, Sakti ekky rino fajar, Subianto mochamad, pada tahun 2020 [6], dengan judul rekonstruksi citra kendaraan menggunakan srcnn untuk peningkatan akurasi pengenalan pelat nomor kendaraan. Pada penelitian ini metode yang digunakan srcnn untuk menigkatkan akurasi pengenalan pelat nomor.…”

Section: Pendahuluanunclassified

Pengolahan Citra untuk Identifikasi Pelat Nomor Kendaraan Mobil Menggunakan Metode Haar Cascade dan Optical Character Recognition

Maulana¹,

Nishom²,

Af'idah³

2022

BITe

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Citra merupakan bentuk informasi yang diperlukan manusia selain teks, suara dan video. Informasi yang terkandung dalam sebuah citra di interpresentasikan berbeda-beda oleh manusia satu dengan lainnya. Metode Haar Cascade sebuah teknik dalam mengidentifikasi suatu objek dengan menggunakan data training berupa citra, terdapat dua dataset yaitu positif dan negatif. Optical character recognition merupakan teknik pengidentifikasian karakter terhadap objek dan hasil dari identifiasi tersebut akan berupa teks. Penelitian ini bertujuan untuk memudahkan pencatatan Kendaraan dan meningkatkan keamanan dalam sebuah tempat parkir. Hasil dari penelitian ini berupa website yang dapat melakukan identifikasi pelat nomor Kendaraan mobil, sistem ini nantinya dapat memudahkan dalam pencatatan kendaraan dalam tempat parkir. Setelah website ini di buat dan dilakukan pengujian menggunakan data uji mendapatkan tingkat keberhasilan 82,80% menunjukan metode yang di gunakan dapat melakukan identifikasi dengan baik

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Low Resolution License Plate Recognition Based on Intelligent Data Processing and Prediction Algorithm

2022

IoT and Big Data Technologies for Health Care

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Vehicle images reconstruction using SRCNN for improving the recognition accuracy of vehicle license plate number

Cited by 3 publications

References 20 publications

YOLOv8 and Faster R-CNN Performance Evaluation with Super-resolution in License Plate Recognition

YOLOv8 and Faster R-CNN Performance Evaluation with Super-resolution in License Plate Recognition

Pengolahan Citra untuk Identifikasi Pelat Nomor Kendaraan Mobil Menggunakan Metode Haar Cascade dan Optical Character Recognition

Low Resolution License Plate Recognition Based on Intelligent Data Processing and Prediction Algorithm

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