Identifying Document Images with Glare Using Global and Localized Feature Fusion

Abstract

This paper presents a framework to identify whether a document image is perturbed with glare. Glare identification for document images is particularly challenging because of predominantly white background and dearth of training dataset. We addresses the dataset bottleneck by introducing a glare synthesis framework to generate a large training dataset. The proposed training model consists of a global deep neural network supplemented by extracted localized feature. To our best knowledge, this is one of the first works towards classifying document image for presence of glare. Experiments on real glare dataset showcase benefits of combined global and local features and also outperform recent glare segmentation model adapted for the classification task.