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In this paper, the task of combining recognition results from multiple images is considered. Systems in which such problems occur are analyzed, and some known approaches are described. It should be noted that currently there is no unified approach that could be used to solve the combination problem for increasing text recognition accuracy using multiple images or in a video stream. As an example, a comparative study of three different approaches to the combination of per-frame recognition results of identity document fields is presented, and it is demonstrated that different approaches may be advantageous for different parts of a data set, while a selection of the potential best single result still significantly outperforms all of the analyzed methods. The task of the per-frame combination of recognition results is an important component in video stream recognition systems and requires careful consideration and the formulation of general approaches that would be applicable to various domains. [ABSTRACT FROM AUTHOR]

Identity documents recognition is far beyond classical optical character recognition problems. Automated ID document recognition systems are tasked not only with the extraction of editable and transferable data but with performing identity validation and preventing fraud, with an increasingly high cost of error. A significant amount of research is directed to the creation of ID analysis systems with a specific focus for a subset of document types, or a particular mode of image acquisition, however, one of the challenges of the modern world is an increasing demand for identity document recognition from a wide variety of image sources, such as scans, photos, or video frames, as well as in a variety of virtually uncontrolled capturing conditions. In this paper, we describe the scope and context of identity document analysis and recognition problem and its challenges; analyze the existing works on implementing ID document recognition systems; and set a task to construct a unified framework for identity document recognition, which would be applicable for different types of image sources and capturing conditions, as well as scalable enough to support large number of identity document types. The aim of the presented framework is to serve as a basis for developing new methods and algorithms for ID document recognition, as well as for far more heavy challenges of identity document forensics, fully automated personal authentication and fraud prevention.