On The Robustness Of A Rectification Algorithm For Documents With A Single Crease

Modern document digitization systems tend to widely apply recognition tasks for camera-captured images. Documents in such images may appear too distorted for an OCR system to be executed on it straightforwardly. This creates the need for an image rectification step. It is particularly essential if the document in the image is mechanically distorted. One of the most common real-life document distortions is the presence of a single arbitrary crease on the paper sheet. The accuracy of a content-independent algorithm for document rectification is heavily dependent on the document localization step. Investigating the properties for document rectification is a critical and useful task, though doing it independently from the localization mistakes can appear complicated. In this paper, we propose to simulate the imperfections of a document localization subsystem by applying Gaussian noise to the manual annotation. It is shown that an actual localization algorithm based on semantic segmentation well fits the proposed model, making it possible to utilize it for the development of a real document recognition system.