Method for Analyzing the Structure of Noisy Images of Administrative Documents

The problem of extracting content elements (fields) from the images of administrative documents via descriptions of anchoring elements is considered. Administrative documents contain static elements and content elements (filled information). The static objects of the document model are the lines of the document structure and the words. Sets of objects united by properties and relationships are described. The text descriptor can contain attributes that distinguish it from similar descriptors. We suggest using combined descriptors of line segments and words. We showed experimentally that the extraction of object sets improves the recognition accuracy of the document fields by 17% and the accuracy of information extraction by 16%. For optical character recognition, we employed
SDK Smart Document Engine in the experiment.

Keywords

noisy image; document recognition; special text point; descriptor.

References

1. Rusinol M., Frinken V., Karatzas D., Bagdanov A.D., Llados J. Multimodal Page Classification Inadministrative Document Image Streams. International Journal on Document Analysis and Recognition, 2014, vol. 17, no. 4, pp. 331-341. DOI: 10.1007/s10032-014-0225-8
2. Jain R., Wigington C. Multimodal Document Image Classification. Document Analysis and Recognition, 2019, vol. 2019, pp.71-77. DOI: 10.1109/ICDAR.2019.00021
3. Qasim S.R., Mahmood H., Shafait F. Rethinking Table Recognition Using Graph Neural Networks. Computer Vision and Pattern Recognition, 2019, vol. 1, pp. 142-147. DOI: 10.1109/ICDAR.2019.00031
4. Bellavia F. SIFT Matching by Context Exposed. Transactions on Pattern Analysis and Machine Intelligence, 2022, vol. 2022, pp. 1-17. DOI: 10.1109/TPAMI.2022.3161853
5. Bay H., Tuytelaars T., Luc Van Goolab. Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 2006, vol. 110, no. 3, pp. 404-417. DOI: 10.1016/j.cviu.2007.09.014
6. Slavin O., Andreeva E., Paramonov N. Matching Digital Copies of Documents Based on OCR. Control and Modeling Problems, 2019, vol. 2019, pp. 177-181. DOI: 10.1109/CSCMP45713.2019.8976570
7. Slavin O., Arlazarov V., Tarkhanov I. Models and Methods Flexible Documents Matching Based on the Recognized Words. Cyber-Physical Systems: Advances in Design and Modelling, 2021, vol. 350, pp. 173-184. DOI: 10.1007/978-3-030-67892-0_15
8. Deza M.M., Deza E. Encyclopedia of Distances. Berlin, Springer-Verlag, 2009.
9. Matas J., Galambos C., Kittler J. Robust Detection of Lines Using the Progressive Probabilistic Hough Transform. Computer Vision and Image Understanding, 2000, vol. 78, issue 1, pp. 119-137. DOI: 10.1006/cviu.1999.0831
10. Grompone von Gioi R., Jakubowicz J., Morel J.M. On Straight Line Segment Detection. Journal of Mathematical Imaging and Vision, 2008, vol. 32, pp. 313-347. DOI: 10.1007/s10851-008-0102-5
11. Grompone von Gioi R., Jakubowicz J., Morel J.M., Randall G. LSD: A Fast Line Segment Detector with a False Detection Control. Transactions on Pattern Analysis and Machine Intelligence, 2010, vol. 32, issue 4, pp. 722-732. DOI: 10.1109/TPAMI.2008.300
12. Emaletdinova L., Nazarov M. Construction of a Fuzzy Model for Contour Selection. Studies in Systems, Decision and Control, 2022, vol. 417, pp. 243-246. DOI: 10.1007/978-3-030-95116-0_20
13. Zlobin P., Chernyshova Y., Sheshkus A., Arlazarov V.V. Character Sequence Prediction Method for Training Data Creation in the Task of Text Recognition. Machine Vision, 2021, vol. 2021, article ID: 120840, 10 p. DOI: 10.1117/12.2623773
14. Matalov D., Usilin S., Arlazarov V.V. About Viola-Jones Image Classifier Structure in the Problem of Stamp Detection in Document Images. Machine Vision, 2021, vol. 2021, article ID: 11605, 16 p. DOI: 10.1117/12.2586842
15. Arlazarov V., Voysyat Ju.S., Matalov D., Nikolaev D., Usilin S.A. Evolution of the Viola-Jones Object Detection Method: A Survey. Bulletin of the South Ural State University. Mathematical Modelling, Programming and Computer Software, 2021, vol. 14, no. 4, pp. 5-23. DOI: 10.14529/mmp210401
16. Roy P.P., Pal U., Llados J. Seal Detection and Recognition: An Approach for Document Indexing. Document Analysis and Recognition, 2015, vol. 2015, article ID: 367879, 15 p. DOI: 10.1109/ICDAR.2009.128
17. Katsuhiko U. Extraction of Signature ad Seal Imprint from Bankchecks by Using Color Information. Document Analysis and Recognition, 1995, vol. 1995, pp. 665-668. DOI: 10.1109/ICDAR.1995.601983
18. Matalov D., Usilin S., Arlazarov V.V. Modification of the Viola-Jones Approach for the Detection of the Government Seal Stamp of the Russian Federation. Machine Vision, 2019, vol. 2019, article ID: 10411, 11 p. DOI: 10.1117/12.2522793
19. Marchenko A.E., Ershov E.I., Gladilin S.A. The System for Parsing a Document Specified by Attributes of Structural Elements and the Rrelations between Structural Elements. Trudy ISA RAN, 2017, vol. 67, no. 4, pp. 87-97. (in Russian)