Document image rectification problem is crucial in document analysis.Most of the current state-of-the-art methods addressing it are data-driven and rely on neural network approaches.However, despite satisfactory rectifications, such methods’ time performance is poor, making them unsuitable for mobile on-device acquisition.The present work concentrates on a specific (but common) case of document physical distortion – the documents with a single crease.We investigate the properties of a surface comprised of two planes captured by a pinhole camera.
Namely, we provide the methods to obtain the ealisboa.com transformation between such an image and the template image having successfully localized opi the color that keeps on giving the document in a frame.It can be utilized in on-device recognition systems: it takes only 3 ms to estimate transformation parameters and about a quarter of a second to rectify an image on a smartphone CPU.We propose a novel dataset FDI-AC containing 200 real images of documents with a single crease in different positions.We conduct experiments comparing our approach with the current state-of-the-art setting a baseline performance on FDI-AC.These experiments show that the proposed algorithm outperforms image rectification transformer network GeoTr in rectification accuracy and time performance.