Method of determining the necessary number of observations for video stream documents recognition

Arlazarov, Vladimir V.; Bulatov, Konstantin B.; Manzhikov, Temudzhin; Slavin, Oleg; Janiszewski, Igor

doi:10.1117/12.2310132

Cited by 7 publications

(12 citation statements)

References 8 publications

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“…Two variations of the stopping method proposed in [14] can be realized -the first, denoted hereinafter as N CX which treats input observations x 1 , x 2 , . .…”

Section: Discussionmentioning

confidence: 99%

“…Figure 2. Quality maps for stopping method described in [14] in two implementation variations: clusterization of integrate results (left) and of the per-frame results (right). Black line designates the best achievable result One of the main disadvantages of this stopping methods is that it is unclear how to jointly select the values for all thresholds to achieve the highest efficiency.…”

Section: Discussionmentioning

confidence: 99%

“…The optimal stopping problems themselves occupy a special place in mathematical statistics and decision theory [12,13]. Some methods were also proposed for the video stream recognition problem [14,15]. In [14] a method was presented which consisted of clustering the set of per-frame field recognition results, estimating a confidence score for each cluster, and making a stopping decision based on three parameters: cluster size, cluster confidence, and the total number of processed observations.…”

Section: Introductionmentioning

confidence: 99%

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

Bulatov

Savelyev

Arlazarov

2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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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.

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“…Two variations of the stopping method proposed in [14] can be realized -the first, denoted hereinafter as N CX which treats input observations x 1 , x 2 , . .…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bulatov

Savelyev

Arlazarov

2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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“…As a baseline a simple counting stopping rule N K was evaluated, which stops at a fixed stage K . Additionally, two variations of stopping rule explored in [2] were evaluated. Since the original paper relies on recognition result confidence estimations, which are unavailable in the scope of this paper, the stopping rule described in [2] is reduced to thresholding at stage n the size of the largest cluster of identical recognition results accumulated up to stage n. Thus we constructed the stopping rule N CX , which thresholds the largest cluster of identical frame recognition results among x 1 , .…”

Section: Evaluation Of Stopping Rulesmentioning

confidence: 99%

“…The stopping problem is particularly important in relation to real-time computer vision systems working on a mobile device [5,17,24], where the time required to obtain the result is often as important as the accuracy of the result itself. Although there are some published works mentioning multiple OCR results integration [6] and the video stream OCR stopping problem [2,5], the corpus of tested and evaluated methods related to this task is hardly sufficient. Examples of text field frames as appeared in the a video stream.…”

Section: Introductionmentioning

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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Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a Video

Bulatov¹,

Fedotova²,

Arlazarov³

2021

2020 25th International Conference on Pattern Recognition (ICPR)

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Method of determining the necessary number of observations for video stream documents recognition

Cited by 7 publications

References 8 publications

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

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

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

Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a Video

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