Abstract: A method and system are provided for removing noise from document images using a neural network-based machine learning model. A dataset of original document images is used as an input source of images. Random noise is added to the original document images to generate noisy images, which are provided to a neural network-based denoising system that generates denoised images. Denoised images and original document images are evaluated by a neural network-based discriminator system, which generates a predictive output relating to authenticity of evaluated denoised images. Feedback is provided backpropagation updates to train both the denoising and discriminator systems. Training sequences are iteratively performed to provide the backpropagation updates, such that the denoising system is trained to generate denoised images that can pass as original document images while the discriminator system is trained to improve the accuracy in predicting the authenticity of the images presented.

Abstract: A method and system are provided for removing noise from document images using a neural network-based machine learning model. A dataset of original document images is used as an input source of images. Random noise is added to the original document images to generate noisy images, which are provided to a neural network-based denoising system that generates denoised images. Denoised images and original document images are evaluated by a neural network-based discriminator system, which generates a predictive output relating to authenticity of evaluated denoised images. Feedback is provided backpropagation updates to train both the denoising and discriminator systems. Training sequences are iteratively performed to provide the backpropagation updates, such that the denoising system is trained to generate denoised images that can pass as original document images while the discriminator system is trained to improve the accuracy in predicting the authenticity of the images presented.