In this paper, we propose a model for assessing the reliability of simultaneous recognition for two text fields with the same content in a printed document. An example of such pairs could be the fields «amount» and «amount in words». The model analytically assesses the probability of independent recognition results in several fields to be coherent, while the fields in reality may be coherent and not coherent. We suggest a method for evaluating a single character recognition reliability that allows for a given multicharacter word recognition reliability threshold.
document recognition, error probability evaluation, probabilistic model for document text field recognition, character recognition, document authentication.
PP. 3-12.
Reference
1. Augereau O, Journet, Domenger J-P. Semi-structured document image matching and recognition. The International Society for Optical Engineering 2013; 8658: 865804. DOI: 10.1117/12.2003911.
2. Binmakhashen G, Mahmoud S.Document Layout Analysis: A Comprehensive Survey. ACM Computing Surveys 2019; 52: 1-36. DOI: 10.1145/3355610. 10.1145/3355610.
3. Naoum A, Nothman J, Curran J. Article Segmentation in Digitised Newspapers with a 2D Markov Model. 2019 International Conference on Document Analysis and Recognition(ICDAR) 2019: 1007-1014. DOI: 10.1109/ICDAR.2019.00165.
4. Průša D, Fujiyoshi A. Rank-Reducing Two-Dimensional Grammars for Document Layout Analysis. 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) 2017: 1120-1125. DOI: 10.1109/ICDAR.2017.185. 10.1109/ICDAR.2017.185.
5. Ravagli J, Ziran Z, Marinai S. Text Recognition and Classification in Floor Plan Images. International Conference on Document Analysis and Recognition Workshops (ICDARW) 2019: 1-6. DOI: 10.1109/ICDARW.2019.00006.
6. Zanibbi R, Blostein D, Cordy JR. A survey of table recognition. International Journal on Document Analysis and Recognition (IJDAR) 2004; 7(1): 1-16. DOI: DOI: 10.1007/s10032-004-0120-9.
7. Couasnon B, Lemaitre A. Recognition of tables and forms. In: Handbook of Document Image Processing and Recognition, Ed by Doermann D, Tombre K. 2014: 647-677. DOI:10.1007/978-0-85729-859-1.
8. Alvear-Sandoval RF, Sancho-Gómez JL, Figueiras-Vidal AR. On improving CNNs performance: The case of MNIST. Information Fusion 2019; 52: 106-109. DOI: 10.1016/j.inffus.2018.12.005.
9. Ignatov A, Timofte R, Chou W, Wang K, Wu M, Hartley T, van Gool L. AI Benchmark: Running Deep Neural Networks on Android Smartphones. Computer Vision – ECCV 2018 Workshops: 288-314. DOI: 10.1007/978-3-030-11021-5_19.
10. Alippi C, Disabato S, Roveri M. Moving Convolutional Neural Networks to Embedded Systems: the AlexNet and VGG-16 case. 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) 2018: 212-223. DOI: 10.1109/IPSN.2018.00049.
11. Xu W, Zhong S, YanL, Wu F, Zhang W. Moving object detection in aerial infrared images with registration accuracy prediction and feature points selection. Infrared Physics & Technology 2018; 92: 318-326. DOI: 10.1016/j.infrared.2018.06.023.
12. Liu Y, Fung K-C, Ding W, Guo H, Qu T, Xiao C. Novel Smart Waste Sorting System based on Image Processing Algorithms: SURF-BoW and Multi-class SVM. Computer and Information Science 2018; 11(3): 35-49. DOI:10.5539/cis.v11n3p35.
13. Roy PP, Pal U, Llados J. Document Seal Detection using GHT and Character Proximity Graphs. Pattern Recognition 2011; 44(6): 1282-1295. DOI: 10.1016/j.patcog.2010.12.004.
14. Micenkova B, Beusekom J, Shafait F. Stamp Verification for Automated Document Authentication. International Workshop on Computational Forensics 2014: Computational Forensics 2015: 117-129. DOI: 10.1007/978-3-319-20125-2_11.
15. PRADO - Public Register of Authentic identity and travel Documents Online /
http://www.consilium.europa.eu/prado/http://www.consilium.europa.eu/prado/ (accessed April 20, 2022).
16. Glossary technical terms related to security features and to security documents in general /
http://www.consilium.europa.eu /prado/en/pradoglossary/prado-glossary.pdf
http://www.consilium.europa.eu/prado/en/pradoglossary/prado-glossary.pdf (accessed April 20, 2022).
17. Hadley, G. (1964). Nonlinear and dynamic programming,. Reading, Mass: Addison-Wesley Pub. Co.
18. Bulatov KB, Bezmaternykh PV, Nikolaev DP, Arlazarov VV. Towards a unified framework for identity documents analysis and recognition. Computer Optics 2022; 46(3): 436-454. DOI: 10.18287/2412-6179-CO-1024
19. Sencar HT, Memon N. Overview of state-of-the-art in digital image forensics. In Book: Bhattacharya BB, Sur-Kolay S, Nandy SC, Bagchi A, eds. Statistical science and interdisciplinary research: Volume 3. Algorithms, architectures and information systems security. Singapore: World Scientific Publishing Co Pte Ltd; 2009: 325-347. DOI: 10.1142/9789812836243-0015
20. Piva A. An overview on image forensics. ISRN Signal Process 2013; 2013: 68-73. DOI: 10.1155/2013/496701
21. Centeno AB, Terrades OR, Canet JL, Morales CC. Identity document and banknote security forensics: A survey. arXiv preprint, 2019. Source: https://arxiv.org/abs/1910.08993
22. Ferreira WD, Ferreira CB, da Cruz Júnior G, Soares F. A review of digital image forensics. Comput Electr Eng 2020; 85: 106685. DOI: 10.1016/j.compeleceng.2020.106685
23. Bulatov, Konstantin & Bezmaternykh, Pavel & Nikolaev, Dmitry & Arlazarov, Vladimir. (2022). Towards a unified framework for identity documents analysis and recognition. Computer Optics. 46. 436-454. 10.18287/2412-6179-CO-1024.
