To achieve this task, let's understand the architecture. We need to recognise the license plate, here specifically Indian, all the contries have different type of license plate character and shape of plate. So, overall we need to detect the number plate first, then recognise the characters. We also have one more bifurcation in this i.e. all the vehicle have different shape and size of number plate. In this task, we are focusing only for car, and any number plate which is of the shape and size of car's number plate in India. Once, we have the plate recognised we have to recognise the characters of the plate. Finally, we have to feed these recognised numbers to a API which can give us the vehicle information.
Image --> HaarCascade indian_license_plate --> Extracted Plate --> Character Segmentation --> DataSet of characters of Indian License Plate --> CNN Model Training --> model.predict(character segmented) --> Extracted plate number
Here, HaarCascade will only detect the number plate of size and shape related to car's number plate.
Import model and necessary function --> New Image of Car --> HaarCascade --> Extracted Plate --> Character Segmentation --> model.predict(segmented character) --> API
Import model and necessary function --> Take Video From user --> For each frame --> HaarCascade --> Extracted Plate --> Character Segmentation --> model.predict(segmented character) --> API --> Final Output on Webpage
http://www.carregistrationapi.in/
As discussed earlier we have to train the model to recognise the plate's characters i.e. alphabets and numbers. Creating such a model need few things which will be explained in here. The standard procedure to extarct any objects from the image is by finding the contours.
Contours can be simply explained as a curve joining all the continuous points (along the boundary), having same colour or intensity. The contours are a useful tool for shape analysis and object detection and recognition.
- For better accuracy, use binary images. So before finding contours, apply threshold or canny edge detection. Since OpenCV 3.2 and later, findContours() no longer modifies the image source.
- In OpenCV, finding contours is like finding white object from black background. So remember, object to be found should be white and background should be black.
Read more at: https://docs.opencv.org/4.5.2/d4/d73/tutorial_py_contours_begin.html