DATA PROCESSING AND ANALYSIS
INTELLIGENCE SYSTEMS AND TECHNOLOGIES
V. L. Arlazarov, O. A. Slavin Issues of Recognition and Verification of Text Documents
MATHEMATICAL MODELING
MANAGEMENT AND DECISION MAKING
SOFTWARE ENGINEERING
V. L. Arlazarov, O. A. Slavin Issues of Recognition and Verification of Text Documents
Abstract. 

The paper deals with the issues of using the analysis of text documents when creating systems for input and recognition of business paper documents. Two main tasks are considered: determining the type of a recognized document and its structuring. Functions are proposed that should provide decomposition and solution of these problems. The work is devoted to the conceptual issues of the analysis of the text content of the document. If the proposed scheme for using text is accepted, then the implementation of input systems may be different. That is, within the framework of the proposed concept of document analysis, various recognition algorithms and data presentation formats can be used.

Keywords: 

document recognition, recognized word, word comparison.

PP. 55-61.

DOI 10.14357/20718632230306
 
References

1. Shafait, F., Breuel, TM.: The Effect of Border Noise on the Performance of Projection-Based Page Segmentation Methods. In IEEE Transactions on Pattern Analysis and Machine Intelligence 2011. Vol. 33. No 4. pp. 846-851. (2011). https://doi.org/10.1109/TPAMI.2010.194.
2. Melinda, L., Ghanapuram, R., Bhagvati. C.: Document Layout Analysis Using Multigaussian Fitting. 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) 2017. pp. 747-752. (2017).
https://doi.org/10.1109/ICDAR.2017.127.
3. Du, X., Wumo, P., Bui, TD..: Text line segmentation in handwritten documents using Mumford–Shah model. Pattern Recognition. Vol. 42. pp. 3136-3145. (2009).
https://doi.org/10.1016/j.patcog.2008.12.021.
4. Maraj, A., Martin, MV., Makrehchi, M.: A More Effective Sentence-Wise Text Segmentation Approach Using BERT. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. Lecture Notes in Computer Science, Springer, Cham. Vol. 12824. (2021).
https://doi.org/10.1007/978-3-030-86337-1_16
5. Mahamoud, IS., Voerman, J., Coustaty, M., Joseph, A., d’Andecy, VP., Ogier, JM. Multimodal Attention-Based Learning for Imbalanced Corporate Documents Classification. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. Lecture Notes in Computer Science, Springer, Cham. Vol. 12823. (2021).
https://doi.org/10.1007/978-3-030-86334-0_15
6. Nguyen, TTH., Jatowt, A., Coustaty, M., Nguyen, NV., Doucet, A.: Post-OCR error detection by generating plausible candidates. International Conference on Document Analysis and Recognition, ICDAR 2019. pp. 876–881. (2019). https://doi.org/10.1109/ICDAR.2019.00145.
7. Guo, C-Y., Tang, YY., Liu, C-S., Duan, J.: A Japanese OCR post-processing approach based on dictionary matching. International Conference on Wavelet Analysis and Pattern Recognition. pp. 22-26. (2013).
https://doi.org/10.1109/ICWAPR.2013.6599286.
8. Kissos, I, Dershowitz, N.: OCR Error Correction Using Character Correction and Feature-Based Word Classification. 12th IAPR Int. Work. Doc. Anal. Syst. DAS 2016. pp. 198–203. (2016). https://doi.org/10.1109/DAS.2016.44.
9. Bulatov, K., Manzhikov, T., Slavin, O., Faradjev, I., Janiszewski, I.: Trigram-based algorithms for OCR result correction. Proc. SPIE 10341. Ninth International Conference on Machine Vision (ICMV 2016). 103410O, (2017).
https://doi.org/10.1117/12.2268559.
10. Slavin, O., Janiszewski, I.: Extraction of information fields in administrative documents using constellations of special text points. Cyber-Physical Systems: Intelligent Models and Algorithms. Springer Nature Switzerland AG.. Vol. 417, pp. 267–279. (2023). https://doi.org/10.1007/978-3-030-95116-0.
11. Deza, MM., Deza, E.: Encyclopedia of distances // Springer-Verlag, Berlin, 2009, xiv+590 pp.
12. Slavin, O., Andreeva, E., Putincev, D.: Application of modified Levenshtein distance for classification of noisy business document images. The 14th International Conference on Machine Vision (ICMV 2021), November 08-12, 2021. Rome, Italy. Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 120840B (2022) DOI: 10.1117/12.2623437.
13. Postnikov, VV.: Flexible Forms Identification. Proceedings of the 5th German-Russian Workshop on Pattern Recognition and Image Understanding (GRWS98). Hamburg: Infix, 1999.
14. Postnikov, VV.: Identification and Recognition of Documents with a Predefined Structure // Pattern Recognition and Image Analysis. Vol. 13. No. 2. pp. 332–334. (2003).
15. Marchenko A.E., Ershov E.I., Gladilin S.A. Sistema razbora dokumenta, zadannogo atributami strukturnykh elementov i otnosheniyami mezhdu strukturnymi elementami [The system for parsing a document specified by attributes of structural elements and the relations between structural elements]. Trudy ISA RAN. Vol 67, No. 4, pp. 87-97. (2017). (In Russian).
16. Yujian, L., Bo, L.: A Normalized Levenshtein Distance Metric. IEEE Transactions on Pat-tern Analysis and Machine Intelligence. Vol. 29. No. 6, pp. 1091-1095. (2007).
 
2024 / 01
2023 / 04
2023 / 03
2023 / 02

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".