The goal of this project is to train a model in order to take a noisy document image as input and output a clean version of it.
The model is implemented using a UNet-based architecture and is trained on a dataset found on Kaggle. It contains noisy document images and their clean counterparts. It was generated using Augraphy, a tool for generating synthetic data.
The loss function used is the Mean Squared Error and the optimizer is Adam.
Some results from the model are shown below.
