Modelling the flow of character recognition results in video stream.

*(English)*Zbl 1400.94007Summary: The paper considers problems of developing stochastic models consistent with results of character image recognition in video stream. A set of assumptions that define the models structure and properties is stated. A class of distributions, namely the Dirichlet distribution and its generalizations, that set a description of the model components is pointed out; and methods for statistical estimation of the distribution parameters are given. To rank the models, the Akaike information criterion is used. The proposed theoretical distributions are verified vs sample data.

##### MSC:

94A08 | Image processing (compression, reconstruction, etc.) in information and communication theory |

68T10 | Pattern recognition, speech recognition |

68U10 | Computing methodologies for image processing |

94A15 | Information theory (general) |

##### Keywords:

stochastic model; video stream; character recognition; Dirichlet distribution; Akaike criterion; goodness-of-fit Anderson-Darling tests##### Software:

sirt##### References:

[1] | A. Hartl, C. Arth, D. Schmalstieg, “Real-Time Detection and Recognition of Machine-Readable Zones with Mobile Devices”, Proceedings 10th International Conference on Computer Vision Theory and Applications, VISAPP 2015, 2015, 79–87 |

[2] | S. Tian, X.C. Yin, Y. Su, H.W. Hao, “Unified Framework for Tracking Based Text Detection and Recognition from Web Videos”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40:3 (2018), 542–554 |

[3] | Arlazarov V. V., Zhukovsky A. E., Krivtsov V. E., Nikolaev D. P., Polevoy D. V., “Analysis of the Features of Using Stationary and Mobile Small-Sized Digital Video Cameras for Document Recognition”, Information Technology and Computer Systems, 2014, no. 3, 71–81 (in Russian) |

[4] | K. Bulatov, V. Arlazarov, T. Chernov, O. Slavin, D. Nikolaev, “Smart IDReader: Document Recognition in Video Stream”, The 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017, master classes and lessons (November 9–12, Kyoto, Japan, 2017), 2017, 39–44 |

[5] | Bulatov K. B., Kirsanov V., Arlazarov V. et al., “Methods for Integrating the Results of Recognition of Document Text Fields in the Video Stream of a Mobile Device”, RFBR Journal, 2016, no. 4, 109–115 (in Russian) |

[6] | Arlazarov V. L., Marchenko A. E., Sholomov D. L., “Cumulative Contexts in the Recognition Problem”, Proceedings of the Institute of Systems Analysis, Russian Academy of Sciences (ISA RAS), 64:4 (2014), 64–72 (in Russian) |

[7] | Bulatov K. B., “Choosing the Optimal Strategy for Combining Frame-by-Frame Character Recognition Results in a Video Stream”, Information Technology and Computer Systems, 2017, no. 3, 45–55 (in Russian) |

[8] | V. Ricci, Fitting Distributions with R, |

[9] | A. Ongaro, S.A. Migliorati, “Generalization of the Dirichlet Distribution”, Journal of Multivariate Analysis, 114 (2013), 412–426 · Zbl 1258.60020 |

[10] | R. Connor, J.J. Mosimann, “Concepts of Independence for Proportions with a Generalisation of the Dirichlet Distribution”, Journal of the American Statistical Association, 64:325 (1969), 194–206 · Zbl 0179.24101 |

[11] | K.W. Ng, G.-L. Tian, M.-L. Tang, Dirichlet and Related Distributions: Theory, Methods and Applications, Wiley, Chichester, 2011 · Zbl 1234.60006 |

[12] | F. Elfadaly, P. Garthwaite, “Eliciting Dirichlet and Connor–Mosimann Prior Distributions for Multinomial Models”, Test, 22:4 (2013), 628–646 · Zbl 1367.62067 |

[13] | K. Fang, S. Kotz, K.W. Ng, Symmetric Multivariate and Related Distributions, Chapman and Hall, N.Y., 1990 · Zbl 0699.62048 |

[14] | G. Ronning, “Maximum Likelihood Estimation of Dirichlet Distributions”, Journal of Statistical Computation and Simulation, 32:3 (1989), 215–221 · Zbl 0718.62048 |

[15] | A. Robitzsch, Sirt: Supplementary Item Response Theory Models. R Package Version 2.6-9, |

[16] | S. Migliorati, A. Ongaro, G.S. Monti, “A Structured Dirichlet Mixture Model for Compositional Data: Inferential And Applicative Issue”, Statistics and Computing, 27:4 (2017), 963–983 · Zbl 1384.62200 |

[17] | S. Migliorati, A.M. Di Brisco, M. Vestrucci, FlexDir: Tools to Work with the Flexible Dirichlet Distribution. R Package Version 1.0, |

[18] | Y. Li, Goodness-of-Fit Tests for Dirichlet Distributions with Applications, PhD Thesis, Bowling Green State University, 2015 |

[19] | M.A. Stephens, “Goodness of Fit, Anderson-Darling Test”, Encyclopedia of Statistical Sciences, 2006, 4 pp. |

[20] | Lemeshko B. Yu., Lemeshko S. B., Postavalov S. N., Chimitova E. V., Statistics Data Analysis, Simulation and Study of Probabilistic Regularities, Infra-M, Novosibirsk, 2011 (in Russian) · Zbl 1008.62596 |

[21] | Bolshev L. N., Smirnov N. V., Tables of Mathematical Statistics, Nauka, M., 1983 (in Russian) |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.