AUTOMATICS NUMBER PLATE RECOGNITION USING CONVOLUTION NEURAL NETWORK

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used for security, safety, and also commercial aspects such as parking control access, and legal steps for the red light violation, highway speed detection, and stolen vehicle detection. The license plate of any vehicle contains a number of numeric characters recognized by the computer. Each country in the world has specific characteristics of the license plate. Due to rapid development in the information system field, the previous manual license plate number writing process in the database is replaced by special intelligent device in a real-time environment. Several approaches and techniques are exploited to achieve better systems accuracy and real-time execution. It is a process of recognizing number plates using Optical Character Recognition (OCR) on images. This paper proposes a deep learning-based approach to detect and identify the Indian number plate automatically. It is based on new computer vision algorithms of both number plate detection and character segmentation. The training needs several images to obtain greater accuracy. Initially, we have developed a training set database by training different segmented characters. Several tests were done by varying the Epoch value to observe the change of accuracy. The accuracy is more than 95% that presents an acceptable value compared to related works, which is quite satisfactory and recognizes the blurred number plate.

Download Full-text

Real-time Automated Detection and Recognition of Nigerian License Plates via Deep Learning Single Shot Detection and Optical Character Recognition

Computer and Information Science ◽

10.5539/cis.v14n4p11 ◽

2021 ◽

Vol 14 (4) ◽

pp. 11

Author(s):

Kayode David Adedayo ◽

Ayomide Oluwaseyi Agunloye

Keyword(s):

Real Time ◽

Character Recognition ◽

Optical Character Recognition ◽

Character Segmentation ◽

Detection Accuracy ◽

License Plate ◽

Single Shot ◽

Optical Character ◽

License Plate Detection ◽

Detection And Recognition

License plate detection and recognition are critical components of the development of a connected Intelligent transportation system, but are underused in developing countries because to the associated costs. Existing license plate detection and recognition systems with high accuracy require the usage of Graphical Processing Units (GPU), which may be difficult to come by in developing nations. Single stage detectors and commercial optical character recognition engines, on the other hand, are less computationally expensive and can achieve acceptable detection and recognition accuracy without the use of a GPU. In this work, a pretrained SSD model and a tesseract tessdata-fast traineddata were fine-tuned on a dataset of more than 2,000 images of vehicles with license plate. These models were combined with a unique image preprocessing algorithm for character segmentation and tested using a general-purpose personal computer on a new collection of 200 automobiles with license plate photos. On this testing set, the plate detection system achieved a detection accuracy of 99.5 % at an IOU threshold of 0.45 while the OCR engine successfully recognized all characters on 150 license plates, one character incorrectly on 24 license plates, and two or more incorrect characters on 26 license plates. The detection procedure took an average of 80 milliseconds, while the character segmentation and identification stages took an average of 95 milliseconds, resulting in an average processing time of 175 milliseconds per image, or 6 photos per second. The obtained results are suitable for real-time traffic applications.

Download Full-text

Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition

Sensors ◽

10.3390/s19133015 ◽

2019 ◽

Vol 19 (13) ◽

pp. 3015 ◽

Cited By ~ 6

Author(s):

Farman Ullah ◽

Hafeez Anwar ◽

Iram Shahzadi ◽

Ata Ur Rehman ◽

Shizra Mehmood ◽

...

Keyword(s):

Access Control ◽

Character Recognition ◽

Optical Character Recognition ◽

Character Segmentation ◽

License Plate ◽

License Plate Recognition ◽

Ultrasonic Sensors ◽

Optical Character

The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.

Download Full-text

A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Sensors ◽

10.3390/s20010055 ◽

2019 ◽

Vol 20 (1) ◽

pp. 55

Author(s):

Nicole do Vale Dalarmelina ◽

Marcio Andrey Teixeira ◽

Rodolfo I. Meneguette

Keyword(s):

Real Time ◽

Intelligent Transportation Systems ◽

Character Recognition ◽

Optical Character Recognition ◽

Recognition System ◽

Transportation Systems ◽

Identification System ◽

License Plate ◽

Optical Character ◽

Smart Parking

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

Download Full-text

Real Time Indian License Plate Detection using Deep Neural Networks and Optical Character Recognition using LSTM Tesseract

2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) ◽

10.1109/icccis48478.2019.8974469 ◽

2019 ◽

Author(s):

Jaskirat Singh ◽

Bharat Bhushan

Keyword(s):

Neural Networks ◽

Real Time ◽

Character Recognition ◽

Optical Character Recognition ◽

Deep Neural Networks ◽

License Plate ◽

Optical Character ◽

License Plate Detection

Download Full-text

Real‐time optical character recognition on field programmable gate array for automatic number plate recognition system

IET Circuits Devices & Systems ◽

10.1049/iet-cds.2012.0339 ◽

2013 ◽

Vol 7 (6) ◽

pp. 337-344 ◽

Cited By ~ 20

Author(s):

Xiaojun Zhai ◽

Faycal Bensaali ◽

Reza Sotudeh

Keyword(s):

Real Time ◽

Field Programmable Gate Array ◽

Character Recognition ◽

Optical Character Recognition ◽

Recognition System ◽

Optical Character ◽

Field Programmable ◽

Gate Array

Download Full-text

Word and Chracter Segmentation in Devnagari and Odia Script – A Comparitive Analysis

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i7060.079920 ◽

2020 ◽

Vol 9 (9) ◽

pp. 377-382

Keyword(s):

Character Recognition ◽

Optical Character Recognition ◽

Research Area ◽

Character Segmentation ◽

National Language ◽

Eastern India ◽

Regional Language ◽

Optical Character ◽

Active Research ◽

Active Research Area

Optical Character Recognition has been an active research area in computer science for several years. Several research works undertaken on various languages in India. In this paper an attempt has been made to find out the percentage of accuracy in word and character segmentation of Hindi (National language of India) and Odia is one of the Regional Language mostly spoken in Odisha and a few Eastern India states. A comparative article has been published under this article. 10 sets of each printed Odia and Devanagari scripts with different word limits were used in this study. The documents were scanned at 300dpi before adopting pre-processing and segmentation procedure. The result shows that the percentage of accuracy both in word and character segmentation is higher in Odia language as compared to Hindi language. One of the reasons is the use of headers line in Hindi which makes the segmentation process cumbersome. Thus, it can be concluded that the accuracy level can vary from one language to the other and from word segmentation to that of the character segmentation.

Download Full-text