Numeric Character Recognition System for Chilean License Plates in semicontrolled scenarios

Olmí, H.; Urrea, Claudio; Jamett, Marcela

doi:10.2991/ijcis.2017.10.1.28

Cited by 2 publications

(2 citation statements)

References 19 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A template matching algorithm was then used to recognize the license plate text from the segmented region and achieved a recognition accuracy of 79.30%. A hybrid segmentation method combining fuzzy logic and k-means clustering was proposed by Olmí et al [43] for vehicle license plate region extraction. They developed SVM and ANN models to perform the classification task and achieved an accuracy level of 95.30%.…”

Section: Recognition or Classificationmentioning

confidence: 99%

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Islam

Raj

Islam

et al. 2020

Sensors

View full text Add to dashboard Cite

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

show abstract

Section: Recognition or Classificationmentioning

confidence: 99%

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Islam

Raj

Islam

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Brillantes et al [19] used the 3-Class Fuzzy Clustering with Thresholding and Connected Component Analysis and were recognized using Template Matching. Olmí et al [20] Proposed segmentation method based on fuzzy logic, Stefanovi et al [21] Proposed character segmentation using K-mean clustering analysis. Part III: Character recognition for each segment.…”

Section: Introductionmentioning

confidence: 99%

License Plate Recognition via Attention Mechanism

Wang¹,

Cao²,

Zou³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

License plate recognition technology use widely in intelligent traffic management and control. Researchers have been committed to improving the speed and accuracy of license plate recognition for nearly 30 years. This paper is the first to propose combining the attention mechanism with YOLO-v5 and LPRnet to construct a new license plate recognition model (LPR-CBAM-Net). Through the attention mechanism CBAM (Convolutional Block Attention Module), the importance of different feature channels in license plate recognition can be re-calibrated to obtain proper attention to features. Force information to achieve the purpose of improving recognition speed and accuracy. Experimental results show that the model construction method is superior in speed and accuracy to traditional license plate recognition algorithms. The accuracy of the recognition model of the CBAM model is increased by two percentage points to 97.2%, and the size of the constructed model is only 1.8 M, which can meet the requirements of real-time execution of embedded low-power devices. The codes for training and evaluating LPR-CBAM-Net are available under the open-source MIT License at: https:// github.com/To2rk/LPR-CBAM-Net.

show abstract

Numeric Character Recognition System for Chilean License Plates in semicontrolled scenarios

Cited by 2 publications

References 19 publications

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

License Plate Recognition via Attention Mechanism

Contact Info

Product

Resources

About