Thai License Plate Recognition Based on Deep Learning

Puarungroj, Wichai; Boonsirisumpun, Narong

doi:10.1016/j.procs.2018.08.168

Cited by 40 publications

(11 citation statements)

References 16 publications

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“…The characters and digits of the rows were segmented using vertical projection with the threshold value. Finally, template matching was used for recognizing [22], [23], [24] the characters and digits of the license plate. Wang et al [25] used the Roberts operator for edge detection and morphological erosion, image clustering and region filling for plate detection from complex backgrounds.…”

Section: Recent Workmentioning

confidence: 99%

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Tadić¹,

Király²,

Ódry³

et al. 2020

ACTA POLYTECH HUNG

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This paper presents a comparative study of improvements to the algorithms for license plate extraction from images captured using conventional, modest-quality cameras. It compares the results and efficacy of two similar algorithms which primarily differ in the raw image pre-processing stage of the initial image. One algorithm uses the Gabor filter bank with distinctly crisp parameters; the other relies on the fuzzified Gabor filter bank with fuzzified parameters to facilitate their adjustment. Results indicate that the fuzzy reasoning introduced for Gabor filter parameter adjustment improves the detection of the components of interest in complex images and adds minimal deviation compared to the Gabor filter bank with distinctly crisp parameters.

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Section: Recent Workmentioning

confidence: 99%

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Tadić¹,

Király²,

Ódry³

et al. 2020

ACTA POLYTECH HUNG

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show abstract

“…The deep convolutional neural network (CNN) architectures learn a hierarchy of discriminate features automatically that richly describe image content. In recent years, the use of deep learning frameworks [20,21,[33][34][35] are also seen in ALPR system because of its powerful recognition capability.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

Asif

Khan

2020

Applied Sciences

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License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.

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“…For accuracy improvement of helmet detection PCA technique is used [14]. For detecting license plate and extracting the characters several methods have been used such as OCR, MobileNets and Inception-v3, Open ALPR [20], [18], [16].…”

Section: Detection Of Non-helmet Riders and Extraction Of License Plamentioning

confidence: 99%

Detection of Non-Helmet Riders and Extraction of License Plate Number using Yolo v2 and OCR Method

J.*¹,

Tejas²,

Varshad³

et al. 2019

IJITEE

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In current situation, we come across various problems in traffic regulations in India which can be solved with different ideas. Riding motorcycle/mopeds without wearing helmet is a traffic violation which has resulted in increase in number of accidents and deaths in India. Existing system monitors the traffic violations primarily through CCTV recordings, where the traffic police have to look into the frame where the traffic violation is happening, zoom into the license plate in case rider is not wearing helmet. But this requires lot of manpower and time as the traffic violations frequently and the number of people using motorcycles is increasing day-by-day. What if there is a system, which would automatically look for traffic violation of not wearing helmet while riding motorcycle/moped and if so, would automatically extract the vehicles’ license plate number. Recent research have successfully done this work based on CNN, R-CNN, LBP, HoG, HaaR features,etc. But these works are limited with respect to efficiency, accuracy or the speed with which object detection and classification is done. In this research work, a Non-Helmet Rider detection system is built which attempts to satisfy the automation of detecting the traffic violation of not wearing helmet and extracting the vehicles’ license plate number. The main principle involved is Object Detection using Deep Learning at three levels. The objects detected are person, motorcycle/moped at first level using YOLOv2, helmet at second level using YOLOv3, License plate at the last level using YOLOv2. Then the license plate registration number is extracted using OCR (Optical Character Recognition). All these techniques are subjected to predefined conditions and constraints, especially the license plate number extraction part. Since, this work takes video as its input, the speed of execution is crucial. We have used above said methodologies to build a holistic system for both helmet detection and license plate number extraction.

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Thai License Plate Recognition Based on Deep Learning

Cited by 40 publications

References 16 publications

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

Detection of Non-Helmet Riders and Extraction of License Plate Number using Yolo v2 and OCR Method

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