Project under Piyush Rai to auto grade answer scripts. Uses
- OpenCV to preprocess images
- TensorFlow to train the Deep Learning model
- MERN Stack to act as the interface to upload answer sheets.
python>=2.7
npm>=6
We first process the images before sending them into our ML model. We smoothen the image using Gaussian Blur to remove noise so that our Sobel Operators can detect edges well. Used Canny Edge Detection to identify the bounding boxes around the written text.
We used adam as our optimizer to train our deep learning model. We used a sparse categorical cross-entropy loss function since our classes were mutually exclusive. We used the EMNIST Dataset to train our models, (including some local samples) to train our model and were able to achieve an accuracy of upto 98%
Training of the model
In order to serve as an interface to upload answer sheets as well as view marks, we created a website using nodejs. We used express to handle our routes for the server and Express Handlebars as a templating engine.
