Context Driven Geometry Consistent Document Reconstruction from Photographs

Abstract

It is very practical to photograph and store documents using mobile phones. However, it is difficult to obtain a quality document image due to creases on the paper and limitations of the camera pose. These produce geometric distortions and irregular shadows on the document image. The rectification of geometric distortions requires an estimate of the 3D shape of the photographed document. In this study, we introduce a new approach that can estimate the 3D shape of the document using artificial neural networks. Neural network models extract geometric information from the context of the image to create a 3D shape. In addition, an adaptive thresholding algorithm was used to correct lighting-related distortions. Data reflecting actual document conditions were used to train the neural networks. Therefore, in addition to previous studies, the method can be applied to photograph samples which creased in many different ways and photographed from varying perspectives. Comparative experiments show that the method works well.