An Automated Approach for the Recognition of Bengali License Plates

• URL: http://arxiv.org/abs/2109.00906v1
• Date: Wed, 1 Sep 2021 17:31:33 GMT
• Title: An Automated Approach for the Recognition of Bengali License Plates
• Authors: Md Abdullah Al Nasim, Atiqul Islam Chowdhury, Jannatun Naeem Muna, Faisal Muhammad Shah
• Abstract summary: This study proposes a hybrid method for detecting license plates using characters from them. Our captured image information was used for the recognition procedure in Bangladeshi vehicles.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Automatic Number Plate Recognition (ALPR) is a system for automatically identifying the license plates of any vehicle. This process is important for tracking, ticketing, and any billing system, among other things. With the use of information and communication technology (ICT), all systems are being automated, including the vehicle tracking system. This study proposes a hybrid method for detecting license plates using characters from them. Our captured image information was used for the recognition procedure in Bangladeshi vehicles, which is the topic of this study. Here, for license plate detection, the YOLO model was used where 81% was correctly predicted. And then, for license plate segmentation, Otsu's Thresholding was used and eventually, for character recognition, the CNN model was applied. This model will allow the vehicle's automated license plate detection system to avoid any misuse.

Related papers

• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Chaurah: A Smart Raspberry Pi based Parking System [0.7646713951724011]
  We propose Chaurah, a minimal cost Automatic Number Plate Recognition (ANPR) system that relies on a Raspberry Pi 3. The primary locates and recognises license plates from a vehicle image, while the secondary performs Optical Character Recognition (OCR) to identify individualized numbers from the number plate. An application built with Flutter for database administration and license plate record comparison makes up the second component of the overall solution.
  arXiv  Detail & Related papers  (2023-12-28T08:34:45Z)
• Bengali License Plate Recognition: Unveiling Clarity with CNN and GFP-GAN [0.0]
  LPR is a system that automatically reads and extracts data from vehicle license plates. A dataset of 1292 images of Bengali digits and characters was prepared for this project.
  arXiv  Detail & Related papers  (2023-12-17T12:28:30Z)
• Drone navigation and license place detection for vehicle location in indoor spaces [55.66423065924684]
  This work is aimed at creating a solution based on a nano-drone that navigates across rows of parked vehicles and detects their license plates. All computations are done in real-time on the drone, which just sends position and detected images that allow the creation of a 2D map.
  arXiv  Detail & Related papers  (2023-07-19T17:46:55Z)
• Indian Commercial Truck License Plate Detection and Recognition for Weighbridge Automation [0.0]
  This paper provides a database on commercial truck license plates, and using state-of-the-art models in real-time object Detection: You Only Look Once Version 7. We have achieved 95.82% accuracy in our algorithm implementation on the presented challenging license plate dataset.
  arXiv  Detail & Related papers  (2022-11-23T18:28:12Z)
• Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection [55.12894776039135]
  State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies. We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain. We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
  arXiv  Detail & Related papers  (2021-03-26T01:18:11Z)
• Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh [0.0]
  This paper presents a YOLOv4 object detection model in which the Convolutional Neural Network (CNN) is trained and tuned for detecting the license plate of the vehicles of Bangladesh. Here we also present a Graphical User Interface (GUI) based on Tkinter, a python package.
  arXiv  Detail & Related papers  (2020-12-03T19:16:49Z)
• Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning [70.84106972725917]
  The proposed solution is cost-effective and can easily replace solutions based on radiofrequency identification (RFID) The system is able to automatically reject some of the train wagons successfully counted, as they have damaged identification codes.
  arXiv  Detail & Related papers  (2020-10-30T14:56:54Z)
• Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal [87.11988786121447]
  This paper presents a framework for 3D object detection and tracking for autonomous vehicles. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack.
  arXiv  Detail & Related papers  (2020-08-21T20:36:21Z)
• Deep Learning Based Traffic Surveillance System For Missing and Suspicious Car Detection [0.0]
  This paper presents a deep learning based automatic traffic surveillance system for the detection of stolen/suspicious cars. It mainly comprises of four parts: Select-Detector, Image Quality Enhancer, Image Transformer, and Smart Recognizer. The effectiveness of the proposed approach is tested on the government's CCTV camera footage, which resulted in identifying the stolen/suspicious cars with an accuracy of 87%.
  arXiv  Detail & Related papers  (2020-07-17T07:18:12Z)
• A Robust Attentional Framework for License Plate Recognition in the Wild [95.7296788722492]
  We propose a robust framework for license plate recognition in the wild. It is composed of a tailored CycleGAN model for license plate image generation and an elaborate designed image-to-sequence network for plate recognition. We release a new license plate dataset, named "CLPD", with 1200 images from all 31 provinces in mainland China.
  arXiv  Detail & Related papers  (2020-06-06T17:11:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.