This work is devoted to an identity document recognition system design for use in mobile phones and tablets using the computational capabilities of the device itself. Key differences are discussed in relation to conservative cloud recognition systems which commonly use single images as an input by design. A mobile recognition system chart is presented which is constructed with computational limitations in mind and which is implemented in a commercial solution. An original approach designed to improve recognition precision and reliability using post-OCR results integration in video stream, as opposed to approaches which rely on frame image integration using "super-resolution" algorithms. An interactive feedback between the system and its operator is discussed, such as automatic video stream recognition stopping decision. Experimental results are presented for an implemented commercial system "Smart IDReader" designed for identity documents recognition.

The article presents an approach to the control of a UAV on the basis of 3D landmark observations. The novelty of the work is the usage of the 3D RANSAC algorithm developed on the basis of the landmarks’ position prediction with the aid of a modified Kalman-type filter. Modification of the filter based on the pseudo-measurements approach permits obtaining unbiased UAV position estimation with quadratic error characteristics. Modeling of UAV flight on the basis of the suggested algorithm shows good performance, even under significant external perturbations.

Various government and commercial services, including, but not limited to, e-government, fintech, banking, and sharing economy services, widely use smartphones to simplify service access and user authorization. Many organizations involved in these areas use identity document analysis systems in order to improve user personal-data-input processes. The tasks of such systems are not only ID document data recognition and extraction but also fraud prevention by detecting document forgery or by checking whether the document is genuine. Modern systems of this kind are often expected to operate in unconstrained environments. A significant amount of research has been published on the topic of mobile ID document analysis, but the main difficulty for such research is the lack of public datasets due to the fact that the subject is protected by security requirements. In this paper, we present the DLC-2021 dataset, which consists of 1424 video clips captured in a wide range of real-world conditions, focused on tasks relating to ID document forensics. The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. For the presented dataset, benchmark baselines are provided for tasks such as screen recapture detection and glare detection. The data presented are openly available in Zenodo.

In the paper, we present a new method for classifying documents with rigid geometry. Our approach is based on the fast and robust Viola-Jones object detection algorithm. The advantages of our proposed method are high speed, the possibility of automatic model construction using a training set, and processing of raw source images without any pre-processing steps such as draft recognition, layout analysis or binarisation. Furthermore, our algorithm allows not only to classify documents, but also to detect the placement and orientation of documents within an image.