CreditCard_Number_Reader

Steps Involved -

Step 1:Creating a Credit Card Number Dataset

• Exploring the fonts using digit images and OTSU binarisation.
• Creating the dataset directories.
• Writing augmentation functions.[digitAugmentation,add_noise,pixelate,stretch,preprocess]
• Creating 1000 variations to each digit of the font type sampled.This is train data.
• For Test data we are sampling the same dataset.In actual it is preferred to have real life unseen data from another source.

Step 2:Training The Model

• Creating Classifier in Keras using LeNet CNN Architecture
• Train the model for 5 epochs.

Step 3:Extracting A Credit Card from the Background[12 out of 16 digits]

• Using canny edge detection for card boundary.
• Using cv2.findContours() to extract the largest contour.
• Use the function four_point_transform() and order_points() to adjust the perspective of the card.

Step 4:Use our Model to Identify the Digits.

• First load grayscale extracted image and the original color.
• Apply the Canny Edge algorithm.
• Use findCountours to isolate the digits.
• Sort the contours by size.
• Then sort left to right by creating a function that returns the x-cordinate of a contour.(cv2.moments)
• Find the bounding rectange of the contour which gives us an enclosed rectangle around the digit.
• Take each extracted digit, use pre_processing function (which applies OTSU Binarization and re-sizes it) then breakdown that image array so that it can be loaded into our classifier.