Abstract
The present article is concerned with methods of comparison of scanned copies of business documents. Such a problem arises when comparing two copies of business documents signed by two parties to detect possible changes made by one of the parties. This problem is relevant, for example, in the banking sector when concluding contracts in paper form. It considers the partial matching method for the flexible form that allows modifying text attributes and inadvertent modifications of common words. It proposes the method of comparison of two scanned images based on recognition and analyses of N-grams words sequences. The proposed method has been tested on its private data set. The proposed method has demonstrated high quality and reliability of searching for differences in two copies of the same Agreement document.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Saha, R., Mondal, A., Jawahar, C.: Graphical Object Detection in Document Images, pp. 51–58. https://doi.org/10.1109/icdar.2019.00018 (2019)
Ray, A., Sharma, M., Upadhyay, A., Makwana, M., Chaudhury, S., Trivedi, A., Singh, A., Saini, A.: An End-to-End Trainable Framework for Joint Optimization of Document Enhancement and Recognition, pp. 59–64. https://doi.org/10.1109/icdar.2019.00019 (2019)
Jain, R., Wigington, C.: Multimodal Document Image Classification, pp. 71–77. https://doi.org/10.1109/icdar.2019.00021 (2019)
Qasim, S.R., Mahmood, H., Shafait, F.: Rethinking Table Recognition using Graph Neural Networks, pp. 142–147. https://doi.org/10.1109/icdar.2019.00031 (2019)
Moysset, B., Kermorvant, C., Wolf, C.: Learning to detect, localize and recognize many text objects in document images from few examples. IJDAR 21, 161–175 (2018). https://doi.org/10.1007/s10032-018-0305-2
Nagy, G.: Document analysis systems that improve with use. IJDAR 23, 13–29 (2020). https://doi.org/10.1007/s10032-019-00344-x
Sidère, N., Cruz, F., Coustaty M., Ogier, J.-M.: A dataset for forgery detection and spotting in document images. In: Proceeding of Seventh International Conference on Emerging Security Technologies (EST). https://doi.org/10.1109/est.2017.8090394, https://sci-hub.tw/10.1109/EST.2017.8090394 (2017)
Bertrand, R., Terrades, O., Gomez-Kramer, R., Franco, P., Ogier, J.: A conditional random field model for font forgery detection. In: 13th International Conference on Document Analysis and Recognition, Nancy, France. [Online] Available: https://doi.org/10.1109/icdar.2013.29 (2015)
Beusekom, J., Shafait, F., Breuel, T.M.: Automated OCR ground truth generation. In: Proceeding of the 8th IAPR Workshop on Document Analysis Systems, pp. 111–117. Nara, Japan, September. https://sci-hub.tw/10.1109/DAS.2008.59 (2008)
Beusekom, J., Shafait, F., Breuel, T.M.: Document signature using intrinsic features for counterfeit detection. In: Proceedings of the 2nd international workshop on Computational Forensics, ser. IWCF ’08, pp. 47–57. Springer-Verlag, Berlin, Heidelberg. https://link.springer.com/content/pdf/10.1007%2F978–3-540-85303-9_5.pdf (2008)
Ahmed, A.G.H., Shafait, F.: Forgery detection based on intrinsic document contents. 11th IAPR International Workshop on Document Analysis Systems. https://doi.org/10.1109/das.2014.26 (2014)
Andreeva, E., Arlazarov, V.V., Manzhikov, T., Slavin, O.: Comparison of the scanned pages of the contractual documents. In: The 10th International Conference on Machine Austria (ICMV 2017), November 13–15. Vienna, Austria. https://doi.org/10.1117/12.2309458 (2017)
Slavin, O.A.: Using special text points in the recognition of documents. In: Studies in Systems, Decision and Control, vol. 259, pp. 43–53. Springer Nature Switzerland AG. http://doi.org/10.1007/978-3-030-32579-4_4 (2020)
Rodehorst, V., Koschan, A.: Comparison and evaluation of feature point detectors (2006)
Lukoyanov, A., Nikolaev, D., Konovalenko, I.: Modification of YAPE keypoint detection algorithm for wide local contrast range images. In: Tenth International Conference on Machine Vision (ICMV 2017), vol. 10696. International Society for Optics and Photonics, vol. 10696. https://doi.org/10.1117/12.2310243 (2018)
Badino, H., Kanade, T.A.: Head-Wearable “Short-Baseline Stereo System for the Simultaneous Estimation of Structure and Motion”. In: Proceedings of MVA, pp. 185–189 (2011)
Skoryukina, N., Farajev, I., Bulatov, K., Arlazarov, V.V.: Impact of geometrical restrictions in RANSAC sampling on the ID document classification. In: Osten, W., Nikolaev, D., Zhou, J. (ed.) ICMV 2019, 11433 ed., vol. 11433, pp. 1–7. ISSN 0277-786X, ISBN 978-15-10636-43-9. https://doi.org/10.1117/12.2559306 (2020)
Bezmaternykh, P.V., Nikolaev, D.P.: A document skew detection method using fast Hough transform. In: Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330 J. [Online] Available: https://doi.org/10.1117/12.2559069 (2020)
Smart IDReader: Document Recognition in Video Stream. In: Bulatov, K., Arlazarov, V., Chernov, T., Slavin, O., Nikolaev, D. 14th IAPR International Conference on Document Analysis and Recognition, vol. 6, pp. 39–44. IEEE. https://doi.org/10.1109/icdar.2017.347 (2017)
Chernyshova, Y.S., Sheshkus, A.V., Arlazarov, V.V.: Two-step CNN framework for text line recognition in camera-captured images. IEEE Access 8, 32587–32600 (2020). https://doi.org/10.1109/ACCESS.2020.2974051
Tesseract OCR. Documentation. [Online] Available: https://tesseract-ocr.github.io. Accessed 26 Oct 2020
Acknowledgements
The research is carried out with partial financial support of The Russian Foundation for Basic Research (projects: 17-29-03170, 18-07-01384).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Slavin, O., Andreeva, E., Arlazarov, V.V. (2021). Search for Falsifications in Copies of Business Documents. In: Kravets, A.G., Bolshakov, A.A., Shcherbakov, M.V. (eds) Cyber-Physical Systems. Studies in Systems, Decision and Control, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-030-67892-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-67892-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67891-3
Online ISBN: 978-3-030-67892-0
eBook Packages: EngineeringEngineering (R0)