An efficient FPGA implementation of Optical Character Recognition for License Plate Recognition

Jing, Yuan; Youssefi, Bahar; Mirhassani, Mitra; Muscedere, Roberto

doi:10.1109/ccece.2017.7946734

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Therefore, its implementation in ANPR systems will affect their overall performance, since characters processed in the OCR stage of such systems are noisy, as discussed earlier. Additionally, the algorithm reported in [30] has an increased hardware utilization compared to the work proposed in [5] and compared to this work. It uses 43 times the BRAM and twice the number of LUTs used in [5], even though the work in [5] uses a larger network on a bigger input image size.…”

Section: Resultsmentioning

confidence: 69%

“…Yet, it is outperforming other systems in terms of the recognition rate. This is due to the nature of Qatari number plates which includes the ten Arabic digits only, where the systems in [5,14,17,30] are used to recognize English letters and Arabic digits.…”

Section: Resultsmentioning

confidence: 99%

“…For the FP (30,11) fixed-point variable, the absolute error per sample does not reach the threshold as shown in Fig. 11.…”

Section: Fixed-point Fractional Part Optimizationmentioning

confidence: 95%

“…11 and 12 illustrate that their absolute error for the acquired results exceeds three times the selected threshold. However, for the FP (31,11), FP (30,11), and FP (29,11) fixed-point variables, 20 samples are used first. From Fig.…”

Section: Fixed-point Fractional Part Optimizationmentioning

confidence: 99%

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Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system

Farhat

Hommos

Al-Zawqari

et al. 2018

J Image Video Proc.

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Automatic number plate recognition (ANPR) systems are becoming vital for safety and security purposes. Typical ANPR systems are based on three stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). Recently, high definition (HD) cameras have been used to improve their recognition rates. In this paper, four algorithms are proposed for the OCR stage of a real-time HD ANPR system. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning, and vector crossing) and template matching techniques. All proposed algorithms have been implemented using MATLAB as a proof of concept and the best one has been selected for hardware implementation using a heterogeneous system on chip (SoC) platform. The selected platform is the Xilinx Zynq-7000 All Programmable SoC, which consists of an ARM processor and programmable logic. Obtained hardware implementation results have shown that the proposed system can recognize one character in 0.63 ms, with an accuracy of 99.5% while utilizing around 6% of the programmable logic resources. In addition, the use of the heterogenous SoC consumes 36 W which is equivalent to saving around 80% of the energy consumed by the PC used in this work, whereas it is smaller in size by 95%.

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Section: Resultsmentioning

confidence: 69%

Section: Resultsmentioning

confidence: 99%

“…For the FP (30,11) fixed-point variable, the absolute error per sample does not reach the threshold as shown in Fig. 11.…”

Section: Fixed-point Fractional Part Optimizationmentioning

confidence: 95%

Section: Fixed-point Fractional Part Optimizationmentioning

confidence: 99%

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Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system

Farhat

Hommos

Al-Zawqari

et al. 2018

J Image Video Proc.

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“…The ALPR system is a key component of modern transportation facilities. It aids in the provision of smart services, like traffic law enforcement, smart parking, electronic toll collection [5], and border control. The system has problems recognizing license plates due to weather conditions, orientation of captured images, and license plate color, texture, and shape.…”

Section: Introductionmentioning

confidence: 99%

Automatic License Plate Recognition System for Vehicles Using a CNN

Kumar¹,

Pandian²

2022

Computers, Materials &Amp; Continua

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Automatic License Plate Recognition (ALPR) systems are important in Intelligent Transportation Services (ITS) as they help ensure effective law enforcement and security. These systems play a significant role in border surveillance, ensuring safeguards, and handling vehicle-related crime. The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network (CNN). A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other. CNNs are popular for license plate character recognition. However, little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at night. We present an efficient ALPR system that uses a CNN for character recognition. A combination of pre-processing and morphological operations was applied to enhance input image quality, which aids system efficiency. The system has various features, such as the ability to recognize multi-line, skewed, and multifont license plates. It also works efficiently in night mode and can be used for different vehicle types. An overall accuracy of 98.13% was achieved using the proposed CNN technique.

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Android application to retrieve car details from car plate numbers

Ahmed

Majeed

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Computer vision is a field in computer science that has had great success due to the increasing popularity of machine learning. Instead of having a human look at images and decide what they depict, we are able to teach computers to recognize patterns of previous images and see the resemblance in new images. In this paper we will get car all information from his number plates through an Android Application which use image preprocessing and YOLO (You only look once) technique to detect the plates and then optimal character recognition to read the plates and Arabic numbers of the cars in Iraq and we have trained it using darkflow. Every Car has its unique Licensed number plate which will be scanned and give the details about the car. Computer vision can also be used to read alphanumeric characters in images and turn them into text. We have implemented API to detect the text and number in the plates. The purpose of this project is to develop a system for our Government and Police So that If police need details about any car he would easily access it. We have achieved the accuracy of 92.23% which means out of 3500 number plates 3220 read correctly.

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An efficient FPGA implementation of Optical Character Recognition for License Plate Recognition

Cited by 10 publications

References 13 publications

Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system

Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system

Automatic License Plate Recognition System for Vehicles Using a CNN

Android application to retrieve car details from car plate numbers

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