The Utilization of Template Matching Method for License Plate Recognition: A Case Study in Malaysia

Jalil, Norazira A.; Basari, Abd Samad Hasan; Salam, Sazilah; Ibrahim, Nuzulha Khilwani; Norasikin, Mohd Adili

doi:10.1007/978-3-319-07674-4_100

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…Connected Component-based approaches (Chang et al, 2004;G. Casey & Lecolinet, 1996;Giannoukos et al, 2010;Jiao et al, 2009) Template matching based methods (Jalil et al, 2015;Khalil, 2010) consist on comparing the similarity of a given character and templates. The most similar template is then chosen.…”

Section: Segmentation-based Approachesmentioning

confidence: 99%

A two-stage deep neural network for multi-norm license plate detection and recognition

Kessentini

Besbes

Ammar

et al. 2019

Expert Systems with Applications

104

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Section: Segmentation-based Approachesmentioning

confidence: 99%

A two-stage deep neural network for multi-norm license plate detection and recognition

Kessentini

Besbes

Ammar

et al. 2019

Expert Systems with Applications

104

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“…The license plate recognition system is installed in many places with multiple purposes, and even law enforcement is using this application to detect speeding vehicles and conduct monitoring and surveillance from a distance. Jalil et al developed a pipeline to detect and extract Malaysian (Figure 6) licence plates (Figure 7) [23]. In the template matching step of the pipeline Fig.…”

Section: License Plate Recognitionmentioning

confidence: 99%

Template Matching Advances and Applications in Image Analysis

Hashemi¹,

Aghdam²,

Ghiasi³

et al. 2016

Preprint

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In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre-or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition.

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“…In broad terms, a comprehensive licence plate recognition system comprises five components, namely, the acquisition of primary licence plate images, image preprocessing, licence plate localization, character segmentation, and character identification. Based on variations in design concepts across different techniques for licence plate positioning and character recognition, it can be inferred that the current advancements in licence plate recognition and positioning are as follows: (1) Licence plate positioning Early licence plate positioning methods mainly used licence plate positioning to find the licence plate area and extract its background colour to obtain a binary image where each pixel is black or white. On the basis of binary images, character height, width, and tilt angle are detected to obtain accurate character height and tilt angle of the licence plate area and width estimation values of the licence plate character area.…”

Section: Introductionmentioning

confidence: 99%

“…The current licence plate location methods are mainly classified into edge-based detection of licence plates, colour-based detection of licence plates, texture-based licence plate detection, and character-based licence plate detection. (2) Licence plate recognition The early character recognition method was based on character normalisation and template matching [1], which aims to obtain a template that matches the licence plate characters, further determine the recognised characters for grammatical analysis, and obtain more reasonable recognition results. The template matching approach used in the character recognition methodology only requires extracting the global features of the character region without performing character segmentation.…”

Section: Introductionmentioning

confidence: 99%

Research advanced in deep learning-based licence plate recognition and localization

2023

ACE

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The purpose of licence plate recognition is to analyze pictures or videos of moving vehicles to read the plate and identify the vehicle's owner. Traffic data management and smart transportation systems rely heavily on licence plate reading technology. Initial picture capture, image preprocessing, licence plate analysis, character segmentation, and recognition are the building blocks of licence plate recognition. The present analysis centres on the examination of the above key steps. In this paper, we introduce the latest research progress in the implementation of licence plate recognition utilizing deep learning techniques, including the classic framework of licence plate location and character recognition, representative methods, and their advantages and disadvantages. We also perform a quantitative comparison of existing representative methods. Finally, we summarize the challenges in the research domain of licence plate recognition and discuss the future development direction from the aspects of neural network interpretability, more general small sample learning methods, and incremental learning.

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The Utilization of Template Matching Method for License Plate Recognition: A Case Study in Malaysia

Cited by 3 publications

References 7 publications

A two-stage deep neural network for multi-norm license plate detection and recognition

A two-stage deep neural network for multi-norm license plate detection and recognition

Template Matching Advances and Applications in Image Analysis

Research advanced in deep learning-based licence plate recognition and localization

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