Volume 11, no. 2Pages 14 - 28

Modelling the Flow of Character Recognition Results in Video Stream

V.V. Arlazarov, O.A. Slavin, A.V. Uskov, I.M. Janiszewski
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.
Full text
stochastic model; video stream; the character recognition; Dirichlet distribution; Akaike criterion; goodness-of-fit Anderson-Darling tests.
1. Hartl A., Arth C., Schmalstieg D. 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, pp. 79-87. DOI: 10.5220/0005294700790087
2. Tian S., Yin X.C., Su Y., Hao H.W. Unified Framework for Tracking Based Text Detection and Recognition from Web Videos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, vol. 40, no. 3, pp. 542-554. DOI: 10.1109/TPAMI.2017.2692763
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, pp. 71-81. (in Russian)
4. Bulatov K., Arlazarov V., Chernova T.V., Slavin A., Nikolaev D. Smart IDReader: Document Recognition in a Video Stream. The 14th IAPR International conference on document Analysis and Recognition (ICDAR 2017), master classes and lessons: November 9-12, Kyoto, Japan, 2017, pp. 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, pp. 109-115. (in Russian) DOI: 10.22204/2410-4639-2016-092-04-109-115
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), 2014, vol. 64, no. 4, pp. 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, pp. 45-55. (in Russian)
8. Ricci V. Fitting Distributions with R. 2005, 24 p. Available at: https://cran.r-project.org/doc/contrib/Ricci-distributions-en.pdf
9. Ongaro A., Migliorati S. A Generalization of the Dirichlet Distribution. Journal of Multivariate Analysis, 2013, vol. 114, pp. 412-426. DOI: 10.1016/j.jmva.2012.07.007
10. Connor R, Mosimann J. Concepts of Independence for Proportions with a Generalisation of the Dirichlet Distribution. Journal of the American Statistical Association, 1969, vol. 64, no. 325, pp. 194-206. DOI: 10.1080/01621459.1969.10500963
11. Ng K.W., Tian G.-L., Tang M.-L. Dirichlet and Related Distributions: Theory, Methods and Applications. Chichester, Wiley, 2011. DOI: 10.1002/9781119995784
12. Elfadaly F, Garthwaite P. Obtaining Preliminary Dirichlet and Connor - Mosimann Distributions for Polynomial Models. Test, 2013, vol. 22, no. 4, pp. 628-646. DOI: 10.1007/s11749-013-0336-4
13. Fang K., Kotz S., Ng K.W. Symmetric Multivariate and Related distribution. New York, Chapman and Hall, 1989.
14. Ronning G. Maximum Likelihood Estimation of Dirichlet Distributions. Journal of Statistical Computation and Simulation, 1989, vol. 32, no. 3, pp. 215-221. DOI: 10.1080/00949658908811178
15. Robitzsch A. Sirt: Supplementary Item Response Theory Models. R Package Version 2.6-9. Avialable at: https://cran.r-project.org/web/packages/sirt/index.html
16. Migliorati S., Ongaro A., Monti G.S. A Structured Dirichlet Mixture Model for Compositional Data: Inferential and Applicative Issues. Statistics and Computing, 2016, vol. 27, no. 4, pp. 963-983. DOI: 10.1007/s11222-016-9665-y
17. Migliorati C., A. Di Brisco M., Vestrucci M. FlexDir: Tools to Work with the Flexible Dirichlet Distribution. Package R version 1.0. Avialable at: https://cran.r-project.org/web/packages/FlexDir/index.html
18. Li Y. Goodness-of-Fit Tests for Dirichlet Distributions with Applications: PhD Thesis. Bowling Green State University, 2015.
19. Stephens M.A. Goodness of Fit, Anderson-Darling Test. Encyclopedia of Statistical Sciences, 2006. 4 p. DOI: 10.1002/0471667196.ess0041.pub2
20. Lemeshko B.Yu., Lemeshko S.B., Postavalov S.N., Chimitova E.V. Statisticheskiy analiz dannykh, modelirovanie i issledovanie veroyatnostnykh zakonomernostey. Kompyuternyy podkhod [Statistics Data Analysis, Simulation and Study of Probabilistic Regularities]. Novosibirsk, Infra-M, 2011. (in Russian)
21. Bolshev L.N., Smirnov N.V. tTablicy matematicheskoy statistiki [Tables of Mathematical Statistics]. Moscow, Nauka, 1983. (in Russian)