The document outlines a system to automatically detect and recognize vehicle license plates from images or video streams. The system aims to extract the license plate region, preprocess the image, perform optical character recognition to recognize the characters, record the date and time, and store the results in a CSV file. It describes the workflow which includes loading an image, extracting the license plate, preprocessing the image, recognizing characters with OCR, retrieving the date and time, and storing the results in a CSV file to process multiple images.

2. AIM:
 The aim of number plate detection using Python is to develop a
system that can automatically detect and recognize number
plates from images or video streams.

3. Objective:
1.Extract license plate region: Develop algorithms to detect and extract the
region of interest (license plate) from the input images.
2.Image preprocessing: Apply image processing techniques to enhance the
extracted license plate region, such as noise reduction, image resizing, and
contrast adjustment.
3.Optical character recognition (OCR): Implement OCR algorithms or libraries
to recognize the characters on the license plate image and convert them into
text.
4.Date and time recording: Capture the current date and time when the license
plate is read.
5.CSV file storage: Design a mechanism to store the license plate number,
along with the corresponding date and time, in a CSV file.
6.User interface (optional): Develop a user-friendly interface to allow users to

4. Input image
License Plate
Region
Extraction
Image
processing
Optical
Character
Recognition
(OCR)
Date and time
recording
Csv file
storage
Block Diagram:

5. Workflow:
1.Load the input image of the vehicle license plate.
2.Apply number plate extraction techniques like edge detection, contour
analysis, or template matching to localize and extract the license plate region.
3.Apply image processing techniques like image enhancement, binarization, or
noise reduction to improve the visibility and quality of the extracted license
plate region.
4.Use OCR algorithms or libraries like Tesseract to recognize the characters on
the license plate and convert them to text.
5.Retrieve the date and time stamp for the current processing instance.
6.Store the recognized license plate text along with the date and time stamp in
a CSV file, creating a new row with each entry.
7.Repeat the process for subsequent license plate images.

6. OUTPUT:

7. Project done by:
 R S Vetrivel(22EC185)
 M Sri Krishna(22EC164)
 M K Sugan(22EC174)