Iraqi License Plate Detection and Segmentation based on Deep Learning

Abbass, Ghida Yousif; Marhoon, Ali

doi:10.37917/ijeee.17.2.12

Cited by 8 publications

(4 citation statements)

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“…The background image of a coin should be cropped to its maximum and more different shades of a coin should be added in the class to increase the accuracy classification rate. As a future work, the same study also can be applied in recognizing other objects other than coin such as handwriting character [22], car license plate [23], [24] and plant leaf [25]- [27].…”

Section: Resultsmentioning

confidence: 99%

Malaysia coin identification app using deep learning model

et al. 2023

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Most of the human work has been replaced by computers in recent years. With the rise of mobile technology and Internet access, recent developments in machine learning (ML) have designed many algorithms to solve diverse human problems. However, due to a lack of exposure to image processing, identification technology is still not widely employed in Malaysia. This paper outlines the steps involved in creating a mobile application for coin identification using ML. In the literature review, the history of the coins is studied in more depth and the features of already existing coin identification mobile applications are compared by their advantages and disadvantages. In addition, using the neural network model, the classification accuracy of successfully identified coins is recorded and disclosed. This study includes the limitations of the prototype mobile application and future improvements that could be added.

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Section: Resultsmentioning

confidence: 99%

Malaysia coin identification app using deep learning model

et al. 2023

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“…In this paper, a smart system was presented to recognize the license plates of Iraqi cars of the model that appeared in 2012 (the German model). It is a continuation of the previously published paper [24] in which a method for the detection of Iraqi car license plates was presented with a method for splitting the LP and recognizing the color of the left side of the LP.…”

Section: The Proposed Methodsmentioning

confidence: 94%

“…When the Iraqi car license plate entered this stage, some image processing operations were performed on it to cut the plate vertically and horizontally to obtain four parts (the left side part, the upper part -the number part-, the lower part and the lower right part -the governorate part -) [24] as show in Figure 1. Step 1: Enter an image of the license plate part that contains the LP numbers and letters.…”

Section: License Plate Segmentationmentioning

confidence: 99%

Car license plate segmentation and recognition system based on deep learning

Abbass

Marhoon

2022

Bulletin EEI

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Artificial intelligence techniques and computer vision techniques dealt with the issue of automatic license plate recognition (ALPR) that has many applications in important research field. In this paper, the method of recognizing the license plates of Iraqi cars was applied based on deep learning techniques convolutional neural network (CNN). The two database built to identifying Iraqi car plates. First database includes 2000 images of Arabic numbers and Arabic letters. Second database conations 1200 images of the Arabic names for Iraqi governorates. This paper used image-processing techniques to segmenting the numbers, letters and words from the car license plate images and then convert them into two databases that used to train the two CNN. These training CNN used to recognizing the vocabulary of the car license plate. The success rate of the numbers, letters and words recognition was 98%. The overall rate of success of this proposed system in all stages was 97%.

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“…Abbass G, Marhoon A., 14 this research focuses on using Single Shot Detector (SSD) deep learning algorithm to identify Iraqi license plates. Then segmenting the license plate using vertical and horizontal projections.…”

Section: Related Workmentioning

confidence: 99%

Hetero-associative Memory Based New Iraqi License Plate Recognition

Hasan,

Aboud,

Hassoon

et al. 2024

Baghdad Sci.J

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As a result of recent developments in highway research as well as the increased use of vehicles, there has been a significant interest paid to the most current, effective, and precise Intelligent Transportation System (ITS). In the field of computer vision or digital image processing, the identification of specific objects in an image plays a crucial role in the creation of a comprehensive image. There is a challenge associated with Vehicle License Plate Recognition (VLPR) because of the variation in viewpoints, multiple formats, and non-uniform lighting conditions at the time of acquisition of the image, shape, and color, in addition, the difficulties like poor image resolution, blurry image, poor lighting, and low contrast, these must be overcome. This paper proposed a model by using Modify Bidirectional Associative Memory (MBAM), which is one type of Hetero-associative memory, MBAM works in two phases (learning and convergence phases) to recognize the number plate, and this proposed model can overcome these difficulties because MBAM's associative memory has a high ability to accept noise and distinguish distorted images, as well as the speed of the calculation process due to the small size of the network. The accuracy of plate region localization is 99.6%, the accuracy for character segmentation is 98%, and the achieved accuracy for character recognition is 100% in various circumstances

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Iraqi License Plate Detection and Segmentation based on Deep Learning

Cited by 8 publications

References 0 publications

Malaysia coin identification app using deep learning model

Malaysia coin identification app using deep learning model

Car license plate segmentation and recognition system based on deep learning

Hetero-associative Memory Based New Iraqi License Plate Recognition

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