Modelling the Flow of Character Recognition Results in Video Stream
DOI:
https://doi.org/10.14529/mmp180202Ключевые слова:
вероятностная модель, видеопоследовательность, распознавание символов, распределение Дирихле, критерий Акаике, критерий согласия Андерсона - Дарлинга.Аннотация
В данной работе рассматриваются проблемы построения вероятностных моделей, согласованных с результатами распознавания образов символов в видеопоследовательностях. Сформулирована совокупность предположений, определяющих структуру и свойства построенных моделей. Выделен класс распределений, а именно распределение Дирихле и его обобщения, задающих описание компонентов моделей, и приведены методы статистического оценивания параметров указанных распределений. Для ранжирования моделей используется информационный критерий Акаике. Проведена проверка согласия предложенных теоретических распределений выборочным данным.Библиографические ссылки
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
Tian S., Yin X.C., Su Y., Hao H.W. Unied 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
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)
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.
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
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)
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)
Ricci V. Fitting Distributions with R. 2005, 24 p. Available at: https://cran.rproject.org/doc/contrib/Ricci-distributions-en.pdf
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
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
Ng K.W., Tian G.-L., Tang M.-L. Dirichlet and Related Distributions: Theory, Methods and Applications. Chichester, Wiley, 2011. DOI: 10.1002/9781119995784
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
Fang K., Kotz S., Ng K.W. Symmetric Multivariate and Related distribution. New York, Chapman and Hall, 1989.
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
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
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
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.rproject.org/web/packages/FlexDir/index.html
Li Y. Goodness-of-Fit Tests for Dirichlet Distributions with Applications: PhD Thesis. Bowling Green State University, 2015.
Stephens M.A. Goodness of Fit, AndersonDarling Test. Encyclopedia of Statistical Sciences, 2006. 4 p. DOI: 10.1002/0471667196.ess0041.pub2
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)
Bolshev L.N., Smirnov N.V. Tablicy matematicheskoy statistiki [Tables of Mathematical Statistics]. Moscow, Nauka, 1983. (in Russian)
