This document summarizes a student project on automatic number plate recognition (ANPR) using optical character recognition (OCR). The project aims to reduce crime by identifying vehicles. Students created a dataset of license plates and used the Tesseract OCR engine to recognize characters. The system workflow involves capturing license plate images, preprocessing them, extracting characters via OCR, and matching the results to the dataset. The project demonstrates applications for parking management, access control, toll collection and border security. It concludes the system could be improved with higher resolution cameras.

1. Title:
Automatic Number Plate Recognition using
Optical Character Recognition (OCR)

2. Gulraiz
Javaid
Waqas
rasheed
Aftab
Farooq
NUST College of electrical and mechanical engineering , ISB, Pakistan

3.  Introduction
 Aim of the project
 Project timeline
 Technical properties of project
 Hardware and software of project
 System Model
 OCR - Technology
 System Flow Diagram
 Results
 Final output
 Technology used
 Influence of project
 Applications of project
 Conclusion and future scope
 References
Presentation Covers
NUST College of electrical and mechanical engineering

4. INTRODUCTION
Presenter: Gulraiz Javaid

5. ANPR stands for Automatic Number Plate
Recognition.
Technology has different names:
 License Plate Recognition
 Car Registration System
 Intelligent Transport System
Introduction to ANPR

6. Optical Character Recognition (OCR) on images
to read the license plates on vehicles.
ANPR Cameras are specialized types of CCTV
camera.
Introduction to ANPR

7. Basic and Primary Aim of the project was Learning.
 Reduce Crime and the Fear of Crime.
 Reduce Burglaries .
 Reduce Vehicle Crime.
 Reduce shop theft and public damages .
 Reduce motorcycle / traffic related problems
AIM of the project

8. We created our own dataset. Which consist of
different models, colors and different
manufacturers of the cars.
Project DataSet
Sr. No. Class Resolution Bit Depth Color
Focal
Length
(mm)
Size
(mb)
Data
type
45 Honda 1024 * 240 24 Blue 4 1.96 JPEG
46 Honda 1024 * 240 24 White 4 1.93 JPEG
47 Importe
d
1024 * 240 24 Silver-white 4 1.68 JPEG

9. Home
Work on
ANPR
5 Dec 2016
Home Work
on OCR
18 Dec 2016
Created Data
Set & Tool
Selection
26 Dec 2016
Meetings of
project
First –
Facing issues on
15 Jan 2017,
Second -
Problems
discussed on 23
Jan 2017
Project
Completed on
30 Jan 2016
Project Timeline

10. Technical Part of Project
Presenter: Waqas Rasheed

11. System Flow Diagram

12. Final Output:

13. Technologies Used
Application Type:
Windows Forms (Desktop App)
Language:
C#.NET
Libraries:
EmguCV
Tesseract OCR Engine
IDE:
Visual Studio 2015

14. System Model,
Applications, Future Scope
and Conclusion of Project
Presenter: Aftab Farooq

15. Hardware and Software of Project
Software
Hardware

16. Hardware and Software of Project
Software Hardware

17. System Model

18. OCR – Technology
We have used Tesseract OCR Engine.
Few of its important commands is listed below.
dataPath: Trained samples,
Language: eng
tessedit_char_whitelist: "ABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890“
tessedit_char_blacklist:

19.  Parking Management
 Access-control in sensitive organizations
 Tolling at Toll Plaza
 Bordercontrol
Applications of the project

20. System Flow Diagram

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22.  Reduce Crime and the Fear of Crime.
 Reduce Burglaries .
 Reduce Vehicle Crime.
 Reduce shop theft and public damages .
 Reduce motorcycle / traffic related problems
Influence of project

23. Conclusion & Future Scope
The automatic vehicle identification system plays an
important role in detecting security threat.
 The system use series of image processing techniques
for identifying the vehicle from the database stored in
the PC
 The system is implemented in Matlab and it
performance is tested on real images.
 The system robustness and speed can be increase if high
resolution camera is used.

24. 1. Muhammad Tahir Qadri, Muhammad Asif’, “automatic number plate recognition
system for vehical identification using optical character recognition”, 2009
International Conference on Education Technology and Computer .
2. D.Renuka devi, D .KanagapushpavaUi, “Automatic License Plate Recognition”,
2011 IEEE.
References