Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives

Konstantin Bulatov; Boris Savelyev; Vladimir V. Arlazarov

doi:10.1117/12.2559447

31 January 2020 Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives

Konstantin Bulatov, Boris Savelyev, Vladimir V. Arlazarov

Author Affiliations +

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114332M (2020) https://doi.org/10.1117/12.2559447
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

In the field of document analysis and recognition using mobile devices for capturing, and the field of object recognition in a video stream, an important problem is determining the time when the capturing process should be stopped. Efficient stopping influences not only the total time spent for performing recognition and data entry, but the expected accuracy of the result as well. This paper is directed on extending the stopping method based on next integrated recognition result modelling, in order for it to be used within a string result recognition model with per-character alternatives. The stopping method and notes on its extension are described, and experimental evaluation is performed on an open dataset MIDV-500. The method was compares with previously published methods based on input observations clustering. The obtained results indicate that the stopping method based on the next integrated result modelling allows to achieve higher accuracy, even when compared with the best achievable configuration of the competing methods.

Citation Download Citation

Konstantin Bulatov, Boris Savelyev, and Vladimir V. Arlazarov "Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114332M (31 January 2020); https://doi.org/10.1117/12.2559447

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