Shadow Removal for Documents with Reflective Textured Surface

An increasing number of institutions are converting from traditional verification to online digital verification of user documents. In Sri Lanka, this requires clean digital images of documents such as the National Identity Card (NIC), driver’s license etc. which often have background textures and reflective surfaces. Due to human error, uneven natural light and reflectance properties, such document images contain cast shadows which pose a difficulty to further processing. The NIC dataset itself is unique in nature. It has some properties unique to a document image, i.e., dark letters on a light background, and some properties unique to a natural image, i.e., background object textures. Therefore, the target domain or nature of dataset itself is a novelty. For such domain, we propose a shadow removal mechanism based on Dual Hierarchical Aggregation Network (DHAN) and VGG-19 (Convolutional network by Visual Geometry Group) object detection deep learning model. Since previous research is not already done on this specific target area, we do not have a direct benchmark to compare our proposed methodology. Hence, we have experimented our dataset with already existing state of the art models in both shadow removal for document images and shadow removal for natural images. Our proposed model reflects the typical backbone architecture for shadow removal models for natural images when removing shadows while preserving background textures. Our architecture can be directly utilised or added to an already-existing image processing pipeline. Although our target domain is relatively new, for comparison purpose we went comparing our model with a close relative which is shadow removal on natural images. Our architecture results in an overall quality improvement of 12% and 63% improvement in output resolution when compared with the state-of-the-art architecture in shadow removal for natural images.