Volume 34 #3

Contents

1. Variability of the pattern VEP parameters in children without visual impairment and questions of preclinical diagnostics
2. Three tactics for gene therapy of two congenital retinal diseases. Review
3. Discrimination of rippled spectra with different ripple densities
4. Method of iterative tomography reconstruction of cropped sinograms
5. ANALYSIS OF A STOPPING METHOD FOR TEXT RECOGNITION IN VIDEO STREAM USING AN EXTENDED RESULT MODEL WITH PER-CHARACTER ALTERNATIVES
6. Recognition of projectively transformed planar figures. XIV. New methods for projectively-invariant description of ovals, using an H-polar curve and dual points
7. Radial distortion correction for camera submerged under water

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 the modelling of the next integrated recognition result, 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 using the open datasets MIDV-500 and MIDV-2019. The method was compared 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, however the computations required are significant and more research should be targeted on optimizing its implementation.

Key words: recognition in video stream, mobile OCR, stopping rules, decision making, mobile document recognition, anytime algorithms

DOI: 10.31857/S0235009220030026

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