Abstract
The paper describes the problem of stopping the text field recognition process in a video stream, which is a novel problem, particularly relevant to realtime mobile document recognition systems. A decisiontheoretic 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.
1 Introduction
Mobile document analysis systems have become the focus of a wide range of research in the recent years [5, 7, 12, 22]. Modern mobile devices are equipped with highquality cameras and decent computing power which allows them to be used for camerabased document analysis tasks. Document recognition on mobile devices presents a set of challenges such as motion blur, defocus, glares on reflective document surface, insufficient camera resolution for accurate OCR (Optical Character Recognition), and other complications [8, 24]. However, the possibility to process multiple frames in real time can be used to mitigate some of these difficulties. In particular, having the ability to recognize the same object multiple times in different video frames was shown to significantly increase the overall recognition accuracy [6]. Multiple recognition results for the same document object (e.g. text field) can be either sorted in some order of preference or confidence and then a single result with the highest expected accuracy can be selected, or the results can be integrated together to produce the result which could be better than each individual frame result. Figure 1 presents examples of text fields as appeared in video frames, and Table 1 presents the corresponding frame recognition results and the integrated results as they change over time.
While object recognition in a video stream presents some advantages and helps to solve problems related to camerabased image analysis, it creates two novel problems which have to be addressed: the problem of optimal integration of multiple results (or selection of the best result among the candidates obtained from different image frames), and the problem of stopping the video stream recognition process. The stopping problem is particularly important in relation to realtime 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.
From the system’s composition perspective, the process of video stream OCR which independently processes each frame and integrates the recognition result can be regarded as an anytime algorithm [30]. From the desired properties of anytime algorithms, one can expect interruptibility (i.e. the process may be stopped after any frame and currently integrated result will be available as the answer) and monotonicity (i.e. on average the quality of integrated results does not decrease). However, the recognition result, at least in the case studied in this paper, does not have a recognizable quality, i.e. the quality of the current result cannot be determined at run time. It is worth mentioning that for OCR problems there exist other classes of anytime algorithms, such as algorithms which output partial recognition results: first for characters which are easy to recognize, and progressing to the most difficult characters, recognition of which demands higher computation time [19]. Although different from the task considered in this paper, for such cases the problem of optimal stopping is also relevant and also requires attention.
At the same time, there exists a large amount of works dedicated to more general stopping rule problems in mathematical statistics and decision theory [4, 9, 13, 26]. This includes such wellresearched problems as the secretary problem [20], houseselling problem [15], \(S_n/n^1\) problem or the problem of maximizing the average [18] and others. The proofreading problem [13, 14] can be regarded as particularly relevant to the text field recognition in a video stream. This problem goes as follows: a manuscript has been digitized with some number of errors M. A proofreading can be performed for the digitized version, each ith proofreading corrects \(X_i\) mistakes and costs a fixed amount c. Each mistake present in the final version of the text also has an inherent cost. The task is to make a decision after ith proofreading, whether the current version of the digitized manuscript should be published, or the review process should continue, in order to minimize the total expected cost. A number of solutions are proposed for this problem [14, 28], based on different assumptions about the distribution of M and \(X_i\). This problem has several variations and other applications, e.g. to software testing [11].
A certain similarity can be observed between proofreading stopping rule problem and the problem of stopping the video stream recognition process: the text field is recognized on multiple frames such that to obtain the recognition result for next frame some cost must be paid (which could represent the time required to preprocess the next frame and perform another text recognition iteration). Assuming that each time a single accumulated recognition result is defined, after each frame a decision must be made either to pay the additional observation cost and continue the recognition process in hope that the result would improve, or to stop the process and output the currently accumulated result. In this case the expected loss can be represented as a linear combination of expected number of processed frames and a distance between expected recognition result to the correct text field value (in terms of some predefined metric).
There are important differences between a video stream recognition stopping rule problem and the proofreading problem, which have to be considered:

1.
In most formulations of the proofreading problem, the value of \(X_i\) is taken to be nonnegative, i.e. it is assumed that each proofreading either corrects some mistakes or at least does not introduce any new ones. In the text field recognition problem, there is no guarantee that the recognition result on the next frame, or the integrated result after processing the next frame, will always be closer to the correct value. However, it can be assumed that the integration algorithms are usually designed in such a way that the recognition of multiple versions of the object generally have higher accuracy than the recognition of a single image of an object.

2.
The solutions for the proofreading problem or its variations typically rely on the decision maker having the ability to assess either the loss which will be suffered if the stopping decision is made, or the difference in losses between stages, as the observed value of \(X_i\) directly contributes to the loss function. In the case of text recognition in a video stream, however, the distance from the currently obtained result to the correct result could only be assessed using some recognition confidence estimations (which could suffer from overconfidence [21]), or could even be completely unavailable.
The proofreading problem is explored in [14], and it is shown that the optimal stopping rule can be constructed for such problem using a notion of monotone stopping problems. The theory of monotone stopping problems is given in detail in [10, 13] and is used in this paper in order to construct the method for stopping the recognition process.
In summary, the goal of this paper is to explore and describe a decisiontheoretic framework for finding the optimal stopping rule for the video stream text field recognition process, assuming that there are no text recognition confidence estimations available to the decision maker. Section 2 provides a formal problem statement and gives a theoretical background for the notion of monotone stopping problems. Section 3 describes an approach to stopping the video stream recognition process based on estimating the expected distance from the currently obtained integrated recognition result to the corresponding result on the next stage. Section 4 presents an experimental evaluation of the proposed approach on a publicly available identity documents video dataset with comparison to some other stopping rules. Section 5 provides some notes on a generalization of this model which could be a subject for a future work.
2 Theoretical framework
2.1 Problem statement
Consider the task of text field recognition in a video stream. Let \(\mathbb {X}\) be the set of all text field recognition results (i.e. strings over some fixed alphabet), with a defined metric function \(\rho :~\mathbb {X} \times \mathbb {X}~\rightarrow [0, +\infty )\). A text field with correct value \(X^{*}\in \mathbb {X}\) is recognized such that a sequence of random recognition results \(\mathbf {X} = (X_1, X_2, \ldots )\) is observed one result at a time, each observation \(x_i\in \mathbb {X}\) is a realization of \(X_i\). We assume that \(X_1,X_2,\ldots \) have identical joint distribution with \(X^{*}\). Within the scope of this paper, we also assume that there are no confidence estimations available for the text field recognition results.
Let us now define recognition results integrator as a function of several recognition results which produces a single integrated result \(R: \mathbb {X}^{+} \rightarrow ~\mathbb {X}\) (here by \(\mathbb {X}^{+}\) we mean the set of all nonempty sequences of elements from \(\mathbb {X}\)). At any moment n the observations \(X_1 = x_1, \ldots , X_n = x_n\) are obtained and the integrated result \(R_n = R(x_1,\ldots ,x_n)\) can be produced. The process may be stopped at any time \(n > 0\) with a cost:
where \(c_\mathrm{e} > 0\) is the cost of recognition error in terms of distance from the ideal result, and \(c_\mathrm{f} > 0\) is the cost of each observation, representing, for example, the computational time required for the recognition of a text field in a single image.
Since both \(c_\mathrm{e}\) and \(c_\mathrm{f}\) are positive constants, the loss function can be simplified without changing the optimization problem:
The loss associated with stopping at time \(n=0\) (i.e. not taking any observations at all) may be regarded as infinite.
The task is to choose a time to stop the observations in order to minimize the expected loss. Let us formalize the problem using notation used in [13]. A stopping rule can be defined as a sequence of functions:
where \(\forall n: 0 \le \phi _n(x_1, \ldots , x_n) \le 1\). The function \(\phi _n(x_1,\ldots ,x_n)\) represents a conditional probability of stopping at the stage n given that stage n has been reached (i.e. given that the values \(X_1=x_1,\ldots ,X_n=x_n\) have been observed).
Based on the stopping rule \(\varPhi \) and the sequence of observations \(\mathbf {X}\) a random stopping time N can be defined. Let \(P(N=n  \mathbf {X}=(x_1,x_2,\ldots ))\) denote a probability mass function for the stopping time N, i.e. the probability for stopping the process at stage n given the sequence of observations \(\mathbf {X}\). This probability mass function relates to the stopping rule \(\varPhi \) as follows:
Conversely, given the random stopping time N, the stopping rule for \(n\in \{0,1,\ldots \}\) can be expressed as a conditional probability of stopping at stage n given the sequence of observations \(\mathbf {X}\) and given that the process did not stop at an earlier stage:
thus both the sequence of functions \(\varPhi \) and the corresponding random variable N can be used to denote a stopping rule. We will use N from now on.
The problem is to choose a stopping rule N to minimize the expected loss V(N), which can be expressed as follows:
2.2 Principle of optimality
Since \(\rho \) cannot take negative values, \(\forall n: L_n \ge c \cdot n\), and since c is a positive constant, we can assume the following:
Under assumptions (7), it can be shown [9, 13] that the optimal stopping rule exists and it follows the principle of optimality.
Let \(V^{*}_n\) denote the minimal expected loss which can be obtained using a stopping rule N such that \(P(N \ge n) =1\), i.e. using a stopping rule that reaches stage n:
where by \(\textit{E}_n(\cdot )\) for the sake of clarity we denote a conditional expectation \(\textit{E}(\cdot  X_1=x_1,\ldots ,X_n=x_n)\), and \({{\,\mathrm{\mathrm{ess\,inf}}\,}}_{N \ge n}\) denotes an essential infimum over a set of stopping rules which reach stage n. The essential infimum is used here instead of a regular infimum, because in general there are more than a countable number of stopping rules \(N \ge n\) and an infimum over an uncountable set of random variables may not be measurable [13]. Thus, (8) effectively means that \(P(V_n^{*}(x_1,\ldots ,x_n) \le \textit{E}_n(L_N)) = 1\) for all \(N \ge n\) and if Z is any other random variable such that \(\forall N\ge n: P(Z \le \textit{E}_n(L_N)) = 1\) then \(P(Z \le V_n^{*}(x_1,\ldots ,x_n)) = 1\).
The principle of optimality states that it is optimal to stop at stage n if and only if the loss suffered in such case is equal to the minimum expected loss obtainable for any stopping rule which reaches stage n. The connection between the minimum expected loss for any stopping rule \(N \ge n\) and for any stopping rule which does not stop at stage n (and thus reaches stage \(n+1\)) can be expressed in form of an optimality equation:
Using assumptions (7) it can be proven [13] that equation (9) holds and that the following stopping rule is optimal:
In other words, the principle of optimality defines the optimal rule (10) as a rule that calls for stopping at the earliest stage n on which the loss is not higher than the minimal expected loss among all stopping rules which reach stage n.
2.3 Monotone problems
Monotone stopping rule problems [10, 13] are a subset of stopping rule problems defined as follows. Let \(A_n\) denote the event \(\{L_n \le \textit{E}_n(L_{n+1})\}\). The stopping rule problem is said to be monotone if
The condition (11) means that if at some stage n the loss function is not higher that expected loss function at the next stage, then this will be true for all future stages as well.
Consider a stopping rule called onestage lookahead rule (also called a myopic rule):
Rule \(N_{A}\) calls for stopping at stage n if the current loss is not higher than the loss which will be suffered if the process stops at stage \(n+1\). It can be shown [10, 13] that if the stopping rule problem is monotone and it has a finite horizon (i.e. for some fixed \(T<\infty \) any stopping rule must stop when it reaches stage T), then the onestage lookahead rule (12) is optimal, i.e. \(V(N_{A})=V(N^*)\).
In order to construct the method for stopping the video stream recognition process, in the next section we will formulate the conditions under which the problem could be considered monotone, at least from a certain stage, and then propose a stopping rule which approximates the behaviour of the onestage lookahead rule (12).
3 Proposed approach
3.1 Approximation of the optimal stopping rule
Let us make the following requirement for the integrator function R: the expected distances between two consecutive integrated recognition results decrease over time.
In the terminology of anytime algorithms [30] assumption (13) means that the problem has a property of diminishing returns. With such assumption about the integration function R we can show that the stopping rule problem (6) with the loss function (2) becomes monotone starting from a certain stage.
Indeed, let \(B_n\) denote the event \(\{\textit{E}_n(\rho (R_n, R_{n+1}) \le c)\}\) and consider the stopping rule problem (6) starting from stage n on which \(B_n\) occurred for the first time. The events considered in condition (11) take the following form:
With a fixed \(X^{*}\) at stage n using triangle inequality, we can obtain the relationship between the distance from the current result to the ideal, the expected distance to the result on next stage and the expected distance from the next result to the ideal:
If the right side of the inequality obtained in (15) is less than or equal to the constant c, then the left side must be as well, and hence if the event \(B_n\) occurs, the event \(A_n\) (14) must also occur. Moreover, using (13) we can see that if \(B_n\) occurs, then \(B_{n+1}\) will also occur, thus obtaining:
Hence, starting from stage n on which \(B_n\) occurred for the first time, events \(A_n, A_{n+1}, A_{n+2}\ldots \) will occur as well, so the problem can be considered monotone starting from this stage, which means that among all stopping rules which reach stage n the onestage lookahead rule (12) is optimal, if the problem has a finite horizon.
Let us now consider stopping rule which tells the decision maker to stop on the earliest stage on which \(B_n\) occurs:
If \(N_{B}\) tells the decision maker to stop at stage n, then \(N_{A}\) also stops at this stage, and if the problem becomes monotone starting from stage n, decision of \(N_{A}\) is optimal, so the optimal rule \(N^{*}\) also stops at this stage. Moreover, if \(\rho (R_n, X^{*})  \textit{E}_n(\rho (R_{n+1}, X^{*})) > c\) the rule \(N_{B}\) does not stop, and neither does \(N^{*}\) following the principle of optimality. Thus, assuming (13), \(N_{B}\) will not stop preliminary, and if it tells the decision maker to stop, then it is optimal to stop. Figure 2 graphically represents the differences between rules \(N_{B}\) and \(N^{*}\) under possible relationships between \(A_n\) and \(B_n\).
The set of situations in which \(N_{B}\) does not stop and \(N^{*}\) may stop is conditioned by two main drawbacks of \(N_{B}\): it relies on the estimation of difference in loss function values using triangle inequality and thus is inefficient if integrated results degrade (i.e. if \(\rho (R_n, X^{*})  \textit{E}_n(\rho (R_{n+1}, X^{*})) < 0\)), and it performs by thresholding the expected metric which could be without any upper bound. Thus, in our proposed approach we will assume that the metric on the set of all text field recognition results has an upper bound (that is, \(\exists G: \forall x,y \in \mathbb {X}: 0 \le \rho (x,y) \le G\)) and that the integrator function R yields better results over time:
In summary, the proposed approach for stopping rule is, firstly, to estimate the expected distance between the current integrated recognition result \(R_n\) (which is known at stage n) and the unknown next result \(R_{n+1}\), and secondly, to perform thresholding of this estimation, thus approximating the behaviour of rule \(N_{B}\).
3.2 Estimation of the expected distance
As required by the stopping rule \(N_{B}\), on nth stage of the process an estimation has to be provided for the expected distance between integrated results \(\varDelta _n \overset{\mathrm {def}}{=}\textit{E}(\rho (R_n, R_{n+1}))\) given current observations \(X_1=x_1,\ldots ,X_n=x_n\). We will assume that the integrator function R is available and can be used to compute such estimation. In the scope of this paper, we propose to estimate \(\varDelta _n\) by modelling the integrated recognition result on the next stage assuming that the new observation will be close to the ones already obtained:
where \(\delta \) is an external parameter. In general the selection of the method for estimating the next integrated recognition result (or the expected distance between it and the current result) might depend on the nature of the integrator function R and other specifics of the problem. Other versions of such estimation for the case of text field recognition could be considered in future works.
4 Experimental evaluation
4.1 Evaluated integrator and distance metrics
In order to apply the model presented in Sect. 3, we have to define a metric \(\rho \) and an integrator function R for the set of possible text field recognition results \(\mathbb {X}\), i.e. on the set of strings. As metrics we considered strict string equality \(\rho _\mathrm{E}\) and a Normalized Levenshtein Distance \(\rho _\mathrm{L}\) [29]:
where x is the length of the string x and \(\text {levenshtein}(x,y)\) is the Levenshtein distance between strings x and y. The triangle inequality holds for both metrics, and the maximum value of both metrics is 1.
As an integrator function R we evaluated a version of the ROVER (Recognizer Output Voting Error Reduction) method [16], which is used for merging text field recognition results produced with different recognition algorithms [25, 27] and for accumulating text recognition result in a video stream [6]. The algorithm consists of two modules: the alignment module performs an optimal wrapping of each incoming string to a word transition network, then the voting module selects the best result by traversing the network and choosing the best result on each stage. In case of strings integration the transition network can be built by a Levenshteinbased optimal wrapping of the incoming string, and the voting can be frequencybased (as we assume \(\mathbb {X}\) to be a set of strings so there are no character recognition confidence estimation available). To implement such method, an empty symbol should be defined with a fixed voting weight relative to the weight of each symbol of the alphabet. In the performed experiments, we assumed the weight of empty symbol to be 0.6.
4.2 Dataset
The evaluation was performed on a publicly available dataset MIDV500 [1] which contains video clips of 50 identity documents (10 clips per document, 30 frames per clip) with annotated positions and values of text fields. To follow the original publication [1] we analysed four field groups: numeric dates, document number, MRZ (machinereadable zone) lines and Latin name components.
Only the frames on which the document is fully visible were considered; thus, the clips in the analysed dataset had different lengths (from 1 to 30 frames). In order to minimize the extra normalization effects and provide a more clear presentation of the results, each evaluated clip was extended up to the length of 30 using repetitions from the beginning of the clip (thus, all evaluated clips for the performed experiments had the same length).
Each field was cropped from the original image according to the combined ground truth of document boundaries and template text field coordinates, with added margins equal to 10% of the smallest text field dimension. Each text field cropping size corresponded to 300 DPI resolution and was recognized using opensource text recognition engine Tesseract [23] (versions v3.05.01 and v4.0.0) using default parameters for text line recognition in the English language. All character comparison was caseinsensitive, and Latin letter O was treated as equal to the digit 0.
Table 2 for each field group lists the number of unique fields in MIDV500 dataset, the total number of field images (across all frames with fully visible document) and average field image sequence size. The table presents average distances from the frame result \(X_i\) to the correct result \(X^{*}\), and from the integrated result for the whole clip to the correct result \(X^{*}\) before extension (\(R_{\text {last}}\)) and after extension (\(R_{30}\)), for distance metrics \(\rho _\mathrm{E}\) and \(\rho _\mathrm{L}\).
Figure 3 illustrates the average distances from the integrated recognition results to the correct result for all evaluated text fields recognized using Tesseract v3.05.01. The figure shows the significant error decrease over time in both metrics, which can be seen as a practical justification of assumption (18).
Figure 4 illustrates the average decrease in the distance from the integrated recognition result to the correct result \(\textit{E}(\rho (R_n,X^{*}))  \textit{E}(\rho (R_{n+1},X^{*}))\) over time, the expected distance \(\varDelta _n\) between consequent integrated results, and its estimation \(\hat{\varDelta }_n\). In the performed experiments the estimation parameter \(\delta \) (19) with value 0.2 was used for both metrics. The figure shows that although the stopping rule \(N_{B}\) is a very rough approximation of \(N_{A}\), there is a practical justification of assumption (13) and that \(\hat{\varDelta }_n\) (19) provides a decent realtime estimation of \(\varDelta _n\) starting from \(n=2\). The approximation of the stopping rule \(N_{B}\) can now be implemented by thresholding the estimation value \(\hat{\varDelta }_n\).
4.3 Evaluation of stopping rules
In order to evaluate the efficiency of stopping rules, a performance profile can be constructed which would graphically show the change of average number of integrated observations and the corresponding average distance from the obtained result (at stopping time) to the correct one, as the observation cost c is varied. Such performance profile represents a tradeoff between the computational time and accuracy of the integrated recognition results and allows to compare different stopping strategies visually.
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 \(N_\mathrm{{CX}}\), which thresholds the largest cluster of identical frame recognition results among \(x_1,\ldots ,x_n\), and \(N_\mathrm{{CR}}\), which does the same for integrated results \(R_1,\ldots ,R_n\). Finally, the stopping rule \(N_{B}\), constructed in this paper, estimates at stage n the expected distance \(\varDelta _n\) to the next integrated result and stops when the estimation becomes lower or equal to a constant threshold. In the performed experiments the stopping rule \(N_{B}\) only became active starting from stage \(n=2\) (i.e. when the estimation \(\hat{\varDelta }_n\) becomes more justified), and the estimation parameter \(\delta \) (19) with value 0.2 was used for both metrics.
Figure 5 illustrates the stopping rules efficiency for all fields when recognized in a video stream using Tesseract recognition engine v3.05.01. The lower position of the curve signifies the better stopping rule performance. It can be observed that in general the proposed stopping rule \(N_{B}\) outperforms the other evaluated stopping rules, especially in \(\rho _\mathrm{L}\) metric.
Figure 6 shows the performance profiles for the same stopping rules and with the same algorithm parameters, but using Tesseract v4.0.0. It should be noted that the method performs well without modification for two separate versions of the Tesseract recognition engine, which employ different text recognition algorithms.
Table 3 shows the average distance from the integrated result to the correct result at stopping time, which could be achieved using the evaluated stopping rules, with text fields recognized using Tesseract v3.05.01. Columns of the Table 3 represent target intervals for the average number of processed frames, rows represent the evaluated stopping rules, and each cell contain the data point with the smallest average number of observations within the target interval and the corresponding average distance to the correct result in terms of metric \(\rho _\mathrm{L}\). Some cells of the table do not contain any data—this signifies that the corresponding stopping rule could not achieve the average number of observations in the target interval on the evaluated dataset (due to more discrete nature of the thresholded parameter). It can be seen that almost in all target intervals the stopping rule \(N_{B}\) proposed in this paper outperforms the other evaluated stopping rules. The similar result can be observed for fields recognized using Tesseract v4.0.0 (results are presented in Table 4).
Stopping rule performance profiles for separate text field groups are presented in Figs. 7 (for Tesseract v3.05.010 and 8 (for Tesseract v4.0.0).
5 Notes on a generalization
In the problem statement explored in Sect. 2, it was assumed that each new observation has a constant cost \(c_\mathrm{f}\) (1). In the real text field recognition system, this cost depends on the time required to produce the next recognition result, which may increase or decrease from stage to stage. Moreover, the observation cost may depend on the stopping rule itself (e.g. include the time required to compute the estimation \(\hat{\varDelta }_n\)) and on the stopping rules for other objects (e.g. the time required to obtain the next recognition result for a field may depend on whether the recognition process for other fields have stopped and thus on the next frame those fields will not be recognized).
It should be noted that assumption (13) is sufficient but not essential for obtaining \(\forall n > 0: B_n \subset B_{n+1}\), as it would also be sufficient to directly require that if starting from some stage n the expected difference \(\textit{E}_n(\rho (R_n, R_{n+1}))\) becomes bounded by c then it would be true for all later stages. For the purposes of this paper, we assumed (13), but a more general approach may be required in future generalizations of the model.
The model of a recognition result presented in Sect. 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 \(\rho (x_n,X^{*})\) or \(\rho (R_n,X^{*})\). 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 onestage lookahead 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 decisiontheoretic stopping rule problem framework proposed in this paper by incorporating the recognition result confidence information is a subject of a future work.
6 Conclusion
The paper described the task of stopping a video stream recognition process, which is an important and novel problem which has to be addressed in mobile document recognition systems design. We presented a problem statement following the classic formulation of a corresponding problem in decision theory and proposed an original stopping method which treats the text field recognition in a video stream as a monotone stopping rule problem and which relies on approximating the optimal stopping rule by estimating the distance to the next integrated recognition result. The method was evaluated on an openly accessible dataset MIDV500 with Tesseract as a recognition engine. It can be seen that the proposed stopping rule performs better than thresholding of the number of processed frames or the size of the maximal cluster of identical preliminary results, considering that no text field recognition confidence estimations were available to the model.
In a future work, this model can be extended to incorporate confidence estimations which could allow for better approximation of the onestage lookahead stopping rule, on which the proposed method is based. Additionally the model could be generalized as to account for variable observations cost and dependence on the stopping rules for other objects.
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Bulatov, K., Razumnyi, N. & Arlazarov, V.V. On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model. IJDAR 22, 303–314 (2019). https://doi.org/10.1007/s10032019003330
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DOI: https://doi.org/10.1007/s10032019003330