Modelling the Flow of Character Recognition Results in Video Stream

Arlazarov, Vladimir V.; Slavin, Oleg; Uskov, Alexander V.; Janiszewski, Igor

doi:10.14529/mmp180202

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…2 is rather simplified as it does not include any kind of confidence estimations for the overall field recognition result or for separate characters (and thus it is assumed that the decision maker does not have any grounds for estimating the distances ρ(x n , X * ) or ρ(R n , X * ). If the confidence estimations are available, the described model and the stopping rule N B are still valid; however, there could exist a better approximation of the one-stage look-ahead rule N A (12) which relies on text field recognition result confidence and on modelling the behaviour of this confidence in a video stream [3]. The generalization of the decision-theoretic stopping rule problem framework proposed in this paper by incorporating the recognition result confidence information is a subject of a future work.…”

Section: Stopping Methodsmentioning

confidence: 99%

On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model

Bulatov

Razumnyi

Arlazarov

2019

IJDAR

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The paper describes the problem of stopping the text field recognition process in a video stream, which is a novel problem, particularly relevant to real-time mobile document recognition systems. A decision-theoretic framework for this problem is provided, and similarities with existing stopping rule problems are explored. Following the theoretical works on monotone stopping rule problems, a strategy is proposed based on thresholding the estimation of the expected difference between consequent recognition results. The efficiency of this strategy is evaluated on an openly accessible dataset. The results show that this method outperforms the previously published methods based on identical results cluster size thresholding. Notes on future work include incorporation of recognition result confidence estimations in the proposed model and more precise evaluation of the observation cost. Keywords Recognition in video stream • Mobile OCR • Stopping rules • Decision making • Mobile document recognition • Anytime algorithms This work was partially financially supported by Russian Foundation for Basic Research, Projects 17-29-03170 and 17-29-03370.

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Section: Stopping Methodsmentioning

confidence: 99%

On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model

Bulatov

Razumnyi

Arlazarov

2019

IJDAR

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“…Note that the confidence value Q(x) is calculated only using the independent per-frame recognition result and is based on individual character classification estimations. Further study of confidence-based weighting criteria may include more careful analysis of the changes in estimation distributions in a video stream [19]. Figure 2 illustrates text field images, their recognition results, and corresponding weight values according to the two proposed criteria.…”

Section: Base Weighting Functionsmentioning

confidence: 99%

Methods of weighted combination for text field recognition in a video stream

Petrova

Bulatov

Arlazarov

2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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Due to a noticeable expansion of document recognition applicability, there is a high demand for recognition on mobile devices. A mobile camera, unlike a scanner, cannot always ensure the absence of various image distortions, therefore the task of improving the recognition precision is relevant. The advantage of mobile devices over scanners is the ability to use video stream input, which allows to get multiple images of a recognized document. Despite this, not enough attention is currently paid to the issue of combining recognition results obtained from different frames when using video stream input. In this paper we propose a weighted text string recognition results combination method and weighting criteria, and provide experimental data for verifying their validity and effectiveness. Based on the obtained results, it is concluded that the use of such weighted combination is appropriate for improving the quality of the video stream recognition result.

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“…Another possibility for improving the detector quality is search and recognition of barcodes if they are present, or recognition of the price within a video stream and integration of the results as described in [29].…”

Section: Further Researchmentioning

confidence: 99%

A low computational approach for price tag recognition

Aliev¹,

Nikolaev

Kunina³

et al. 2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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In this work we discuss the task of search, localization and recognition of price zone within a photograph of the price tag. The task is being addressed for the case when image is acquired by small-scale digital camera and calculation device has significant resource constraints. The proposed approach is based on Niblack binarization algorithm, analysis and clasterization of connected components in conditions of known price tag geometrical model. The algorithm was tested on a private dataset and has shown high quality.

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Modelling the Flow of Character Recognition Results in Video Stream

Cited by 10 publications

References 10 publications

On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model

On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model

Methods of weighted combination for text field recognition in a video stream

A low computational approach for price tag recognition

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