В работе решается задача распознавания шаблона удостоверяющего документа, которая является одной из ключевых подзадач современных систем распознавания удостоверяющих документов. Представлена концептуальная схема архитектуры распознающей системы, показано место модуля распознавания шаблона документа, а также детально рассмотрены ключевые этапы его обработки.
распознавание документов, идентификационные документы, искусственный интеллект, OCR, распознавание шаблона документа, машинное обучение, обработка изображений.
Стр. 19-25.
Литература
1. Awal A.M., Ghanmi N., Sicre R. and Furon T. Complex document classification and localization application on identity document images // 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). 2017. Vol. 01. P. 426–431.
2. Augereau O., Journet N. and Domenger J.-P. Semi-structured document image matching and recognition // Document Recognition and Retrieval XX, R. Zanibbi and B. Coüasnon. Eds. 2013. Vol. 8658. P. 13–24.
3. Ryan M. and Hanafiah N. An examination of character recognition on id card using template matching approach // Procedia Computer Science. 2015. Vol. 59. P. 520–529.
4. Slavin O.A. Using Special Text Points in the Recognition of Documents. Cham: Springer International Publishing. 2020. P. 43–53.
5. Minkina A., Nikolaev D., Usilin S. and Kozyrev V. Generalization of the Viola-Jones method as a decision tree of strong classifiers for real-time object recognition in video stream // ICMV 2014. 2015. Vol. 9445. No. 944517. P. 1–5.
6. Puybareau E. and Geraud T. Real-time document detection in smartphone videos // 2018 25th IEEE International Conference on Image Processing (ICIP). 2018. P. 1498 -1502.
7. Das Neves Junior R.B., Lima E., Bezerra B.L., Zanchettin C. and Toselli A.H. HU-PageScan: a fully convolutional neural network for document page crop // IET Image Processing. 2020. Vol. 14. P. 3890–3898.
8. Loc C.V., Cao De T., Burie J.C. and Ogier J.M. Content region detection and feature adjustment for securing genuine documents // 12th International Conference on Knowledge and Systems Engineering (KSE). 2020. P. 103–108.
9. Mei J., Islam A., Wu Y., Moh’d A. and Milios E.E. Statistical Learning for OCR Text Correction // arXiv preprint arXiv:1611.06950. 2016.
10. Nguyen T., Jatowt A., Coustaty M., Nguyen N. and Doucet A. Post-OCR error detection by generating plausible candidates // 2019 International Conference on Document Analysis and Recognition (ICDAR). 2019. P. 876–881.
11. Llobet R., Cerdan-Navarro J., Perez-Cortes J. and Arlandis J. OCR post-processing using weighted finite-state transducers // 20th International Conference on Pattern Recognition. 2010. P. 2021–2024.
12. Povolotskiy M.A. and Tropin D.V. Dynamic Programming Approach to Template-based OCR // ICMV 2018. 2019. Vol. 11041. No. 110411T.
13. Zhou X., Yao C., Wen H., Wang Y., Zhou S., He W. and Liang J. EAST: An efficient and accurate scene text detector // 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. P. 2642–2651.
14. Chernyshova Y.S., Sheshkus A.V. and Arlazarov V.V. Two-step CNN framework for text line recognition in camera-captured images // IEEE Access. 2020. Vol. 8. P. 32 587–32 600.
15. Bulatov K.B. A method to reduce errors of string recognition based on combination of several recognition results with per-character alternatives // Bulletin of the South Ural State University, Series: Mathematical Modelling, Programmingand Computer Software. 2019. Vol. 12. No. 3. P. 74–88.
16. Yujian L. and Bo L. A normalized Levenshtein distance metric // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007. Vol. 29. No. 6. P. 1091–1095.
17. Fiscus J.G. A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER) // IEEE Workshop on Automatic Speech Recognition and Understanding. 1997. P. 347–354.
18. Arlazarov V.V., Bulatov K., Manzhikov T., Slavin O. and Janiszewski I. Method of determining the necessary number of observations for video stream documents recognition // Proc. SPIE (ICMV 2017). 2018. Vol. 10696.
19. Bulatov K., Razumnyi N. and Arlazarov V.V. On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model // International Journal on Document Analysis and Recognition. 2019. Vol. 22. No. 3. P. 303–314.
20. Ren H., El-Khamy M. and Lee J. Video super resolution based on deep convolution neural network with two-stage motion compensation // 2018 IEEE International Conference on Multimedia Expo Workshops (ICMEW). 2018. P. 1–6.
