A modification of a stopping method for text recognition in a video stream with best frame selection

Tolstov, Ilya Olegovich; Martynov, S. A.; Farsobina, V V; Bulatov, Konstantin B.

doi:10.1117/12.2586928

Cited by 2 publications

(1 citation statement)

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“…Non-text objects such as personal signatures of photos can also undergo the process of integration T8. For example, this process may perform a selection of a single best image by analyzing the focus score [68,90], consistency of illumination or the presence of highlights [91]. Alternatively, the images extracted from individual frames may be combined into a single image of a higher quality using methods of video super-resolution [92,93].…”

Section: C) Template Processingmentioning

confidence: 99%

Towards a unified framework for identity documents analysis and recognition

Bulatov¹,

Bezmaternykh²,

Nikolaev

et al. 2022

Computer Optics

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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.

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