Proceedings ArticleDOI
Real-Time Automatic License Plate Recognition through Deep Multi-Task Networks
Gabriel Resende Gonçalves,Matheus Alves Diniz,Rayson Laroca,David Menotti,William Robson Schwartz +4 more
- pp 110-117
TLDR
This work proposes a technique that is able to perform ALPR using only two deep networks, the first performs license plate detection (LPD) and the second performslicense plate recognition (LPR), which does not execute explicit character segmentation, which reduces significantly the error propagation.Abstract:
With the increasing number of cameras available in the cities, video traffic analysis can provide useful insights for the transportation segment. One of such analysis is the Automatic License Plate Recognition (ALPR). Previous approaches divided this task into several cascaded subtasks, i.e., vehicle location, license plate detection, character segmentation and optical character recognition. However, since each task has its own accuracy, the error propagation between each subtask is detrimental to the final accuracy. Therefore, focusing on the reduction of error propagation, we propose a technique that is able to perform ALPR using only two deep networks, the first performs license plate detection (LPD) and the second performs license plate recognition (LPR). The latter does not execute explicit character segmentation, which reduces significantly the error propagation. As these deep networks need a large number of samples to converge, we develop new data augmentation techniques that allow them to reach their full potential as well as a new dataset to train and evaluate ALPR approaches. According to experimental results, our approach is able to achieve state-of-the-art results in the SSIG-SegPlate dataset, reaching improvements up to 1.4 percentage point when compared to the best baseline. Furthermore, the approach is also able to perform in real time even in scenarios where many plates are present at the same frame, reaching significantly higher frame rates when compared with previously proposed approaches.read more
Citations
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Journal ArticleDOI
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector
Rayson Laroca,Luiz A. Zanlorensi,Gabriel Resende Gonçalves,Eduardo Todt,William Robson Schwartz,David Menotti +5 more
TL;DR: Li et al. as discussed by the authors presented an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules.
Journal ArticleDOI
Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review
TL;DR: This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques, and identifies promising future directions.
Journal ArticleDOI
Research on License Plate Recognition Algorithms Based on Deep Learning in Complex Environment
Wang Weihong,Tu Jiaoyang +1 more
TL;DR: The application of deep learning in license plate recognition is discussed, and the main work is to introduce the most advanced algorithms from the three main technical difficulties: license plate skew, image noise and license plate blur.
Journal ArticleDOI
Convolutional Neural Networks for Automatic Meter Reading
Rayson Laroca,Victor Barroso,Matheus Alves Diniz,Gabriel Resende Gonçalves,William Robson Schwartz,David Menotti +5 more
TL;DR: In this article, a two-stage approach that employs the Fast-YOLO object detector for counter detection and evaluates three different CNN-based approaches for counter recognition is proposed.
Proceedings ArticleDOI
Real Time Indian License Plate Detection using Deep Neural Networks and Optical Character Recognition using LSTM Tesseract
Jaskirat Singh,Bharat Bhushan +1 more
TL;DR: The goal of this paper is to design a robust technique for License Plate Detection in the images using deep neural networks, Pre-process the detected license plates and perform License Plate Recognition (LPR) using LSTMTesseract OCR Engine and achieve robust results.
References
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