scholarly journals License Plate Recognition with Feature Salience and Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 2985-2990
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Core Component ◽  
Character Extraction ◽  
Recognition Result

Character recognition algorithm is considered as a core component of License Plate Recognition (LPR) systems. Numerous methods for License Plate (LP) recognition have been developed in recent years. However, most of them are not advanced enough to recognize in complex background and still demand improvement. This paper introduces a novel system for LPR by analyzing vehicle images. Accurate segmentation of license plate and character extraction from the plate is accomplished. In the plate segmentation module, Hough transform is put forwarded to identify plate edges using line segments. Radon transform adjusts the skew between LP and the viewer, thereby improve the recognition result. Four features are extracted from the LP image, and best features are selected using feature-salience theory. Histogram projection is performed horizontally and vertically to isolate individual characters in the LP. Finally, Back Propagation Neural Network (BPNN) is used to identify the characters present in the LP. From experimental results, it is evident that the proposed system can recognize LP more efficiently and establish a good background for future advancements in LPR.

BP-Neural Network for Plate Number Recognition

2016 ◽  
Vol 8 (3) ◽  
pp. 34-45 ◽  
Author(s):  
Jia Wang ◽  
Wei Qi Yan
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
License Plate ◽  
License Plate Recognition ◽  
Traffic Surveillance ◽  
Plate Number ◽  
Number Recognition ◽  
Traffic Systems ◽  
Artificial Neural Network Ann

The License Plate Recognition (LPR) as one crucial part of intelligent traffic systems has been broadly investigated since the boosting of computer vision techniques. The motivation of this paper is to probe in plate number recognition which is an important part of traffic surveillance events. In this paper, locating the number plate is based on edge detection and recognizing the plate numbers is worked on Back-Propagation (BP) Artificial Neural Network (ANN). Furthermore, the authors introduce the system implementation and take advantage of the well-known Matlab platform to delve how to accurately recognize plate numbers. There are 80 samples adopted to test and verify the proposed plate number recognition method. The experimental results demonstrate that the accuracy of the authors' character recognition is above 70%.

LICENSE-PLATE RECOGNITION USING DWT AND NEURAL NETWORK

2006 ◽  
Vol 04 (04) ◽  
pp. 601-615 ◽  
Author(s):  
TIAN-DING CHEN
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
High Accuracy ◽  
Discrete Wavelet ◽  
License Plate ◽  
License Plate Recognition ◽  
Structure Modification ◽  
New Approach ◽  
Low Pass

This paper presents a new approach for license-plate recognition using Discrete Wavelet Transform (DWT) and Plastic Perception Neural Network (PPNN). It accomplishes the preliminary license-plate localization by applying low-pass wavelet coefficients. Since the amount of data reduces to 1/4, this approach saves a lot of running time, simplifies computational complexity, and economizes memory usage. It adopts the LL and HH sub-bands, which come from a two-dimensional Haar DWT to implement the localization and segmentation for license plates. The proposed methodology provides high accuracy for locating a license plate from an image, and has a high tolerance for license plate displacement in the images. Back-Propagation Neural Network (BPNN) has the advantage of anti-noise and anti-distortion, but the problems of traditional BPNN are a longer learning period, iterations are not prone to convergence, and local minimum. The proposed methods combine the parallel distributive process concept with the BPNN structure modification to solve the above problems. This paper also utilizes PPNN to solve taking position, scale and rotation of the license-plate recognition.

scholarly journals Bus Attendance System using Optical Character Recognition

2020 ◽  
Vol 9 (4) ◽  
pp. 2133-2136
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
License Plate ◽  
License Plate Recognition ◽  
Surveillance Camera ◽  
Optical Character ◽  
The Neural Network ◽  
Character Extraction ◽  
Image Dataset

In today’s world managing the records of attendance of staffs, students, employee or bus is a tedious task. This project focuses on automating the bus attendance process through vehicle license plate recognition. As, the license plate is a feature that is peculiar to every vehicle, it would help in efficiently marking the bus attendance. The bus attendance system using RFID is a time consuming process. Hence we developed a project to efficiently mark attendance using number plate recognition and OCR. The system was trained using faster RCNN model with bus image dataset. The proposed system is the number plate is captured through surveillance camera and the captured image will be passed as an input to the neural network for training and the number plate will be detected. Character extraction is done using OCR and extracted character matched will be checked with the database and the attendance for particular bus will be marked.

A BACK PROPAGATION BASED REAL-TIME LICENSE PLATE RECOGNITION SYSTEM

2008 ◽  
Vol 22 (02) ◽  
pp. 233-251 ◽  
Author(s):  
YO-PING HUANG ◽  
TSUN-WEI CHANG ◽  
YEN-REN CHEN ◽  
FRODE EIKA SANDNES
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
License Plate ◽  
License Plate Recognition ◽  
Video Frames ◽  
Parking Lot

License plate recognition systems have been used extensively for many applications including parking lot management, tollgate monitoring, and for the investigation of stolen vehicles. Most researches focus on static systems, which require a clear and level image to be taken of the license plate. However, the acquisition of images that can be successfully analyzed relies on both the location and movement of the target vehicle and the clarity of the environment. Moreover, only few studies have addressed the problems associated with instant car image processing. In view of these problems, a real-time license plate recognition system is proposed that recognizes the video frames taken from existing surveillance cameras. The proposed system finds the location of the license plate using projection analysis, and the characters are identified using a back propagation neural network. The strategy achieves a recognition rate of 85.8% and almost 100% after the neural network has been retrained using the erroneously recognized characters, respectively.

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