Automatic Number Plate Detection by using Image Processing

Project Code :TCMAPY539


The main objective of the project is to detect the number plate by using optical character recognition technique.


License plate detection is an image processing technology that uses a license (number) plate for vehicle identification. The objective is to design and implement an efficient vehicle identification system that identifies the vehicle using the vehicle’s license plate. The system can be implemented on the entrance of parking lots, toll booths, or any private premises like college, etc. to keep the records of on-going and outgoing vehicles. It can be used to allow access to only permitted vehicles inside the premises. The developed system first captures the image of the vehicle’s front, then detects the license plate and then reads the license plate. The vehicle license plate is extracted using the image processing of the image. Optical character recognition (OCR) is used for character recognition. The system is implemented using OpenCV and its performance is tested on various images. It is observed that the developed system successfully detects and recognizes the vehicle license plate.

To recognize License number plates using the Python programming language. We will utilize OpenCV for this project in order to identify the license number plates and the python pytesseract for the characters and digits extraction from the plate. We will build a Python program that automatically recognizes the License Number Plate.

Keyword: Vehicles License plate images, Opencv, pytesseract OCR.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram


H/W Specifications:
  • Processor:  I5/Intel Processor
  • RAM:  8GB (min)
  • Hard Disk:  128 GB

S/W Specifications:

  • Operating System:Windows 10
  • Server-side Script:Python 3.6
  • IDE:PyCharm,Jupyter notebook
  • Libraries Used: Numpy, IO, OS, Flask, keras, pandas, tensorflow, OpenCV, pytesseract OCR

Learning Outcomes

  • Practical exposure to
    • Hardware and software tools
    • Solution providing for real time problems
    • Working with team/individual
    • Work on creative ideas
  • Testing techniques
  • Error correction mechanisms
  • What type of technology versions is used?
  • Working of Tensor Flow
  • Implementation of Deep Learning techniques
  • Working of CNN algorithm
  • Working of Transfer Learning methods
  • Building of model creations
  • Scope of project
  • Applications of the project
  • About Python language
  • About Deep Learning Frameworks
  • Use of Data Science

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