CNN-Based Character Recognition for License Plate Recognition System

Pham, Van Huy; Dinh, Phong; Nguyen, Van Huan

doi:10.1007/978-3-319-75420-8_56

Cited by 10 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has the capability to achieve a maximum accuracy of 98.79% on a limited sample count. A CNN-based model was introduced in the study conducted earlier [14] for VLPNR which used several preprocesses like filtering, thresholding, and segmentation.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Based License Plate Number Recognition for Smart Cities

Vetriselvi¹,

Lydia²,

Mohanty³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates. The current research paper presents an effective Deep Learning (DL)-based VLPNR called DL-VLPNR model to identify and recognize the alphanumeric characters present in license plate. The proposed model involves two main stages namely, license plate detection and Tesseract-based character recognition. The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model. Then, the characters in the detected number plate are extracted using Tesseract Optical Character Recognition (OCR) model. The performance of DL-VLPNR model was tested in this paper using two benchmark databases, and the experimental outcome established the superior performance of the model compared to other methods.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Based License Plate Number Recognition for Smart Cities

Vetriselvi¹,

Lydia²,

Mohanty³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Among them, intense sampling of different areas of an image uniformly makes bounding boxes with various aspect ratios and dimensions. The bounding box results are then used with feature extraction using a CNN [ 36 , 37 , 38 ] for classification and regression. Typical vehicle LPR involve the You Only Look Once (YOLO) series [ 39 , 40 , 41 , 42 , 43 , 44 ] and single shot detector [ 45 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

View full text Add to dashboard Cite

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

show abstract

“…including Mahalanobis distance and Hamming distance (Du et al, 2013). These ( Pham et al, 2018) proposes an approach using a CNN classifier for the recognition of license plate characters and uses firstly some pre-processing techniques on input images, such as filtering, thresholding, and then segmentation.…”

Section: Segmentation-based Approachesmentioning

confidence: 99%