Algorithm for choosing the best frame in a video stream in the task of identity document recognition

M.A. Aliev I.A. Kunina A.V. Kazbekov V.L. Arlazarov

Journal: Компьютерная оптика @computer-optics

Section: International conference on machine vision

Article in issue: 1 т.45, 2021.

Free access

During the process of document recognition in a video stream using a mobile device camera, the image quality of the document varies greatly from frame to frame. Sometimes recognition system is required not only to recognize all the specified attributes of the document, but also to select final document image of the best quality. This is necessary, for example, for archiving or providing various services; in some countries it can be required by law. In this case, recognition system needs to assess the quality of frames in the video stream and choose the "best" frame. In this paper we considered the solution to such a problem where the "best" frame means the presence of all specified attributes in a readable form in the document image. The method was set up on a private dataset, and then tested on documents from the open MIDV-2019 dataset. A practically applicable result was obtained for use in recognition systems.

human perception \ quality assessment \ document images \ blur \ sharpness \ flares.

Similar articles in the section Application-oriented computer-based techniques

Document image analysis and recognition: a survey
Document image analysis and recognition: a survey

Arlazarov Vladimir Viktorovich, Andreeva Elena Igorevna, Bulatov Konstantin Bulatovich, Nikolaev Dmitry Petrovich, Petrova Olga Olegovna, Savelev Boris Igorevich, Slavin Oleg Anatolevich

A joint study of deep learning-based methods for identity document image binarization and its influence on attribute recognition
A joint study of deep learning-based methods for identity document image binarization and its influence on attribute recognition

Snchez-rivero R., Bezmaternykh P.V., Gayer A.V., Morales-gonzlez A., Jos silva-mata F., Bulatov K.B.

Short address: https://sciup.org/140253872

IDS: 140253872   |   DOI: 10.18287/2412-6179-CO-811