In this paper we introduce a novel neural network architecture based on Fast Hough Transform layer. The layer of this type allows our neural network to accumulate features from linear areas across the entire image instead of local areas. We demonstrate its potential by solving the problem of vanishing points detection in the images of documents. Such problem occurs when dealing with camera shots of the documents in uncontrolled conditions. In this case, the document image can suffer several specific distortions including projective transform. To train our model, we use MIDV-500 dataset and provide testing results. Strong generalization ability of the suggested method is proven with its applying to a completely different ICDAR 2011 dewarping contest. In previously published papers considering this dataset authors measured quality of vanishing point detection by counting correctly recognized words with open OCR engine Tesseract. To compare with them, we reproduce this experiment and show that our method outperforms the state-of-the-art result.

In this paper we discuss the problem of simultaneous document type recognition and projective distortion parameters estimation for the images of ID documents. There are two considered cases. In the first case a video stream captured using mobile devices is processed on the device. The second case considers photos or scanned images which are processed on a server. For each case the requirements are defined for the input data and processing speed. The universal approach is proposed, which allows solving the problem in both cases. The approach is based on representing the image as a constellation of feature points and descriptors, but in order to perform more accurate distortion parameters estimation straight lines and quadrangles are extracted from the input image and used as additional features. Techniques are described which allow to combine matched feature points, lines, and quadrangles to geometric verification using RANSAC. Best alternative selection criteria are proposed along with methods of solution accuracy estimation. The differences between methods of preliminary analysis of the input image and geometric primitives location are discussed in relation to the considered problems. For quality estimation an open dataset MIDV-500 is used, together with its extension for server-side problem version, created in scope of this work. Results show that using lines and quadrangles increase the location accuracy, and the proposed algorithm surpasses previously published works in classification precision and computational performance.

The paper is devoted to the analysis of the problem of document boundaries detection in images and in a video stream. The paper proposes an algorithm for obtaining the position of the document, consisting of very reliable segments of a document boundaries extraction and a construction of an intersection graph that satisfies the projective model of the rectangle. An online algorithm for selecting and integrating possible document positions in a video stream based on the Kalman filter is proposed. The analysis of possible modifications of the algorithm and their effect on the final result are provided. Evaluation of the quality of the document at ICDAR'15 Smartphone Document Capture competition's dataset [1] showed a mean result of 95.5% in Jaccard index of projectively corrected document quadrangles and a 3rd place in the competition.