Object Recognition using CNN model

Task Description 📄

📌 In this task :

👉Create a model that will detect a car in a live stream or video and recognize characters on number plate of the car .

👉Secondly , it will use the characters and fetch the owners information using RTO API’s .

👉Create a Web portal where all this information will be displayed (using html,css,and js)

📌 Note : You may create your own detection model .

👉 Make a blog/article/video explaining this task step by step

Step 1: Resize the image to the required size and then grayscale it. The code for the same is given below

using a bilateral filter (Blurring) will remove the unwanted details from an image.

edge detection : — The syntax will be destination_image = cv2.Canny(source_image, thresholdValue 1, thresholdValue 2)

Once the counters have been detected we sort them from big to small and consider only the first 10 results ignoring the others. In our image the counter could be anything that has a closed surface but of all the obtained results the license plate number will also be there since it is also a closed surface.

the remaining information is pretty much useless for us. So we can proceed with masking the entire picture except for the place where the number plate is.

Character Segmentation: -

Number Plate Recognition is to segment the license plate out of the image by cropping it and saving it as a new image.