License Plate Recognition System using Neural Networks and Multithresholding Technique

Bhushan, Bharat; Singh, Simranjot; Singla, Ruchi

doi:10.5120/14576-2704

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…Plate localization accuracy of 97.4%, character segmentation accuracy of 96%, and the character recognition accuracy 76% are achieved. The authors addressed the problem of license plate detection using the inception method in [16]. The proposed approach aims at identifying license plates from dull and low-intensity images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The cell in the grid is responsible for predicting the object on which the object is centered [32]. The output of the model will be the vector for [16]. YOLOv3 predicts that there are multiple bounding boxes per grid cell, but selects a bounding box with the greatest union over (IOU) intersection with the true ground, which is called non-maximum suppression.…”

Section: License Plate Localizationmentioning

confidence: 99%

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License plate identification and recognition in a non-standard environment using neural pattern matching

Shafi

Hussain

Ahmad

et al. 2021

Complex Intell. Syst.

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Non-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: License Plate Localizationmentioning

confidence: 99%

License plate identification and recognition in a non-standard environment using neural pattern matching

Shafi

Hussain

Ahmad

et al. 2021

Complex Intell. Syst.

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“…ANNs can be described as computer programs that simulate biological neural networks. ANNs are capable of self-learning [1][2][3][4][5][24][25][26]. These networks have the ability to learn, memorize and create relationships between the information.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…Today, artificial neural networks are used in voice recognition [2,3], license plate recognition [4], fingerprint recognition [5], fault recognition [6,7], motion recognition [8], gender recognition [9] and many classification applications. Another research area that is one of the important recognition systems is handwriting recognition systems [10,11].…”

Section: Introductionmentioning

confidence: 99%

Turkish handwriting recognition system using multi-layer perceptron

Kuncan¹,

Vardar²,

Kaplan³

et al. 2020

J. mechatron., artif. intell., eng.

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Recently, handwriting recognition has found many application areas along with technological advances. Handwriting recognition systems can greatly simplify human life by reading tax returns, forwarding mail, reading bank checks, and so on. On the other hand, these systems can reduce the need for human interaction. Therefore, academic and commercial studies of handwriting characters have recently become an important research topic in pattern recognition. In this study, Turkish handwritten letter recognition system from A to Z was developed in C++ environment by using Artificial Neural Networks (ANNs). After the feature data were extracted, handwriting images were presented to the network, the training process of ANN was completed, and different handwriting images were classified with trained ANN. In this study, MLP (Multi-Layered Perceptron: MLP) type ANN and back-propagation learning algorithm were used. The ANN used has 35 inputs and 23 outputs. In the hidden layer, ANNs with different numbers of artificial neural cells (neurons) were evaluated and the most appropriate neural number ANN was selected. As a result, ANN with 24 neurons was selected in the hidden layer and handwriting images was classified with an accuracy rate of 94.90 %.

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“…Jaringan Saraf Tiruan adalah sekumpulan nodeyang saling terhubung, serupa dengan jaringan neuron yang luas di otak (Bhushan et al, 2013).Penggunaan Jaringan Saraf Tiruan dalam aplikasi pengenalan karakter dapat menyederhanakan kode secara dramatis dan meningkatkan kualitas pengenalan sehingga dapat mencapai kinerja yang baik (Patel., 2013).Selain itu, kelebihan lainnya dari Jaringan Saraf Tiruan adalah sifatnya yang sangat tahan terhadap noise (Varshney et al, 2014).Secara umum, arsitektur dari Jaringan Saraf Tiruan terdiri dari 3 lapisan seperti yang ditampilkan pada Gambar 3 berikut ini.…”

Section: Pengenalan Karakterunclassified

Pengenalan Pola Karakter Plat Nomor Kendaraan Menggunakan Algoritma Momentum Backpropagation Neural Network

Avianto

2016

JIFO

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Abstrak Peningkatan jumlah kendaraan bermotor yang terus terjadi di PENDAHULUANJumlah kendaraan di Indonesia, terutama di kota-kota besar, terus mengalami kenaikan yang signifikan tiap tahunnya. Berdasarkan data milik Korps Lalu Lintas Kepolisian Negara Indonesia yang dikutip oleh situs surat kabar Kompas, pada tahun 2013jumlah kendaraan di Indonesia mencapai 104.211.000 unit, atau meningkat sebesar 11% dibandingkan dengan tahun sebelumnya. Besarnya peningkatan jumlah kendaraan ini ikut memberikan dampak pada munculnya masalah lalu lintas seperti kemacetan lalu lintas. Kemacetan lalu lintas adalah kondisi yang terjadi ketika jumlah kendaraan di jalan melebihi kapasitas jalan, dan ditandai oleh deselerasi, keterlambatan waktu tempuh, dan antrian panjang(Olusina dan Samson, 2014). Selain kemacetan, jumlah tindak pelanggaran lalu lintas juga ikut meningkat. Salah satu solusi yang sudah diterapkan di negara-negara maju adalah Sistem Lalu Lintas Cerdas. Sistem ini dapat digunakan secara luas untuk banyak tujuan, seperti sistem manajemen tol otomatis pada jalan raya, jembatan, terowongan, manajemen kendaraantransportasi perkotaan, komunitas cerdas, manajemen parkir cerdas, validasi plat nomor, deteksi kendaraan yang dicuri, dan statistik lalu lintas (Yingyong et al., 2015).Pengenalan plat nomor merupakan salah satu teknologi penting pada Sistem Lalu Lintas Cerdas (Shih et al., 2012).Teknologi ini memanfaatkan pengolahan citra untuk mengidentifikasi kendaraan dari citra plat nomornya (Singh dan Randhawa, 2014). Namun keragaman pada plat dan lingkungan sekitarnya seperti ukuran font, jenis font, warna font, lokasi plat nomor, dan perbedaan intensitas karena lampu atau lingkungan dapat menyebabkan masalah pada saat pengenalan plat nomor (Ramachandran et al., 2015).

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License Plate Recognition System using Neural Networks and Multithresholding Technique

Cited by 11 publications

References 19 publications

License plate identification and recognition in a non-standard environment using neural pattern matching

License plate identification and recognition in a non-standard environment using neural pattern matching

Turkish handwriting recognition system using multi-layer perceptron

Pengenalan Pola Karakter Plat Nomor Kendaraan Menggunakan Algoritma Momentum Backpropagation Neural Network

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