Efficient Embedded Neural-Network-Based License Plate Recognition System

Caner, Hakan; Geçim, H. Selçuk; Alkar, Ali Ziya

doi:10.1109/tvt.2008.915524

Cited by 114 publications

(58 citation statements)

References 14 publications

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“…5 shows the ROI location. In [3], [10] and [4] Connected Component Labelling (CCL) is used for LPD. CCL scans the image and labels the pixels according to the pixel connectivity.…”

Section: License Plate Detectionmentioning

confidence: 99%

“…There are two types of connectivity:-4 and 8 connectivity. In [3], a feature extraction algorithm is used to count the similar labels to distinguish it as a region. The region with maximum area is considered as a possible license plate region and this region is forwarded to the segmentation process.…”

Section: License Plate Detectionmentioning

confidence: 99%

“…Character segmentation is the procedure of extracting the characters from the LP image. Almost all the papers that had been surveyed [1], [3] and [10] used horizontal and vertical projection to segment the characters. Fig.…”

Section: Character Segmentationmentioning

confidence: 99%

“…Number templates are assigned higher priorities because chances of occurrences of alphabets are less than the numbers. Caner et al in [3] used a Self Organizing Map (SOM) neural network to identify the characters. An ordinary SOM has the following two layers: 1) an input layer and 2) a computation layer.…”

Section: Fig 12 Disambiguous Parts Of Characters [5]mentioning

confidence: 99%

“…LPR system plays an important role in many applications like electronic payment system (toll payment [3] and parking fee payment), to find stolen cars [1], traffic surveillance. For example in parking, number plates are used to calculate the duration of the parking.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Survey on License Plate Recognition Systems

Gilly¹,

Raimond²

2013

IJCA

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License Plate Recognition (LPR) is a well known image processing technology. LPR system consists of four steps: capture the image from digital camera, pre-processing, character segmentation and character recognition. License plates are available in various styles and colors in various countries. Every country has their own license plate format. So each country develops the LPR system appropriate for the vehicle license plate format. Difficulties that the LPR systems face are the environmental and non-uniform outdoor illumination conditions. Therefore, most of the systems work under restricted environmental conditions like fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Each LPR system use different combination of algorithms. From the papers being surveyed, it is realized that a good success rate of 93.7% is obtained by the combination of fuzzy logic for license plate detection and Self Organizing (SO) neural network for character recognition. Comparisons of different LPR systems are discussed in this paper.

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“…5 shows the ROI location. In [3], [10] and [4] Connected Component Labelling (CCL) is used for LPD. CCL scans the image and labels the pixels according to the pixel connectivity.…”

Section: License Plate Detectionmentioning

confidence: 99%

Section: License Plate Detectionmentioning

confidence: 99%

Section: Character Segmentationmentioning

confidence: 99%

Section: Fig 12 Disambiguous Parts Of Characters [5]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on License Plate Recognition Systems

Gilly¹,

Raimond²

2013

IJCA

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Aerial Imaging Rescue and Integrated System for Road Monitoring Based on AI/ML

Kumar,

Jaglan,

Kakde

2024

Advances in Aerial Sensing and Imaging

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License plate recognition using neural architecture search for edge devices

Shashirangana

Padmasiri

Meedeniya

et al. 2021

Int J of Intelligent Sys

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The mutually beneficial blend of artificial intelligence with internet of things has been enabling many industries to develop smart information processing solutions. The implementation of technology enhanced industrial intelligence systems is challenging with the environmental conditions, resource constraints and safety concerns. With the era of smart homes and cities, domains like automated license plate recognition (ALPR) are exploring automate tasks such as traffic management and fraud detection. This paper proposes an optimized decision support solution for ALPR that works purely on edge devices at night‐time. Although ALPR is a frequently addressed research problem in the domain of intelligent systems, still they are generally computationally intensive and unable to run on edge devices with limited resources. Therefore, as a novel approach, we consider the complex aspects related to deploying lightweight yet efficient and fast ALPR models on embedded devices. The usability of the proposed models is assessed in real‐world with a proof‐of‐concept hardware design and achieved competitive results to the state‐of‐the‐art ALPR solutions that run on server‐grade hardware with intensive resources.

show abstract

Efficient Embedded Neural-Network-Based License Plate Recognition System

Cited by 114 publications

References 14 publications

A Survey on License Plate Recognition Systems

A Survey on License Plate Recognition Systems

Aerial Imaging Rescue and Integrated System for Road Monitoring Based on AI/ML

License plate recognition using neural architecture search for edge devices

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