Nom Document Background Removal Using Generative Adversarial Network

In this research, we present a new technique to improve the performance of a Nom-character recognition system. Nom-character recognition is a challenging problem in pattern recognition. Especially these characters are not only blurred or distorted in a paper of a historical document containing ink strokes and symbols created by readers. Generative Adversarial Network (GAN) is one of the advanced versions of deep neural networks applied to generate artificial photos of objects [28]. Many versions of GAN have been malfunctioned recently to help the learning process be more stable and realistic to maximize features extracted from the data. We have been using a recent version of GAN to extract characters from images with complex backgrounds and brightness. This task is to retrieve clean text images from complex and noisy background sources. To the best of our knowledge, we perform the test on the Nom Dataset, which characterizes by multiple noise forms. The results demonstrate that this approach can help to improve any Nom-character recognition system.