Abstract
Plagiarism detection in scholar assignments becomes more and more relevant nowadays. Rapidly growing popularity of online education, active expansion of online educational platforms for secondary and high school education create demand for development of an automatic reuse detection system for handwritten assignments. The existing approaches to this problem are not usable for searching for potential sources of reuse on large collections, which significantly limits their applicability. Moreover, real-life data are likely to be low-quality photographs taken with mobile devices. We propose an approach that allows detecting text reuse in handwritten documents. Each document is a picture and the search is performed on a large collection of potential sources. The proposed method consists of three stages: handwritten text recognition, candidate search and precise source retrieval. We represent experimental results for the quality and latency estimation of our system. The recall reaches 83.3% in case of better quality pictures and 77.4% in case of pictures of lower quality. The average search time is 3.2 s per document on CPU. The results show that the created system is scalable and can be used in production, where fast reuse detection for hundreds of thousands of scholar assignments on large collection of potential reuse sources is needed. All the experiments were held on HWR200 public dataset.
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REFERENCES
A. V. Nikitov, O. A. Orchakov, and Yu. V. Chekhovich, “Plagiarism in works of undergraduate and graduate students: Problem and methods of counteraction,” Univ.skoe Upr.: Prakt. Anal., No. 5, 61–68 (2012).
R. Miguel, “Avoiding plagiarism, self-plagiarism, and other questionable writing practices: A guide to ethical writing,” (2011).
Yu. V. Chekhovich and O. S. Belen’kaya, “Methodology for the implementation and use of text reuse detection systems in secondary education,” Inf. Obraz., No. 10, 5–14 (2021). https://doi.org/10.32517/0234-0453-2021-36-10-5-14
K. Praveen and C. V. Jawahar, “Matching handwritten document images,” in Computer Vision-ECCV 2016, Ed. by B. Leibe, J. Matas, N. Sebe, and M. Welling, Lecture Notes in Computer Science, Vol. 9905 (Springer, Cham, 2016), pp. 766–782. https://doi.org/10.1007/978-3-319-46448-0_46
O. Bakhteev, R. Kuznetsova, A. Khazov, A. Ogaltsov, K. Safin, T. Gorlenko, M. Suvorova, A. Ivahnenko, P. Botov, Yu. Chekhovich, and V. Mottl, “Near-duplicate handwritten document detection without text recognition,” in Computational Linguistics and Intellectual Technologies: Annual Int. Conf. “Dialogue” (Russian State University for the Humanities, 2021), pp. 47–57. https://doi.org/10.28995/2075-7182-2021-20-47-57
O. Pandey, I. Gupta, and B. S. P. Mishra, “A robust approach to plagiarism detection in handwritten documents,” in Advances in Visual Computing, Ed. by G. Bebis, Lecture Notes in Computer Science, Vol. 12510 (Springer, Cham, 2020), pp. 682–693.
D. Coquenet, C. Chatelain, and T. Paquet, “End-to-end handwritten paragraph text recognition using a vertical attention network,” IEEE Trans. Pattern Anal. Mach. Intell. 45, 508–524 (2022). https://doi.org/10.1109/tpami.2022.3144899
V. Rowtula, V. Bhargavan, M. Kumar, and C. V. Jawahar, “Scaling handwritten student assessments with a document image workflow system,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition Workshops (IEEE, 2018), pp. 2307–2314.
P. Voigtlaender, P. Doetsch, and H. Ney, “Handwriting recognition with large multidimensional long short-term memory recurrent neural networks,” in 2016 15th Int. Conf. on Frontiers in Handwriting Recognition (ICFHR), Shanzhen, China, 2016 (IEEE, 2016), pp. 228–233. https://doi.org/10.1109/icfhr.2016.0052
A. Shonenkov, D. Karachev, M. Novopoltsev, M. Po-tanin, and D. Dimitro, StackMix and blot augmentation for handwritten text recognition, arXiv Preprint (2021). https://doi.org/10.48550/arXiv.2108.11667
D. Nurseitov, K. Bostanbekov, D. Kurmankhojayev, A. Alimova, A. Abdallah, and R. Tolegenov, “Handwritten Kazakh and Russian (HKR) database for text recognition,” Multimedia Tools Appl. 80, 33075–33097 (2021). https://doi.org/10.1007/s11042-021-11399-6
I. Potyashin, M. Kaprielova, Y. Chekhovich, A. Kildyakov, T. Seil, E. Finogeev, and A. Grabovoy, “HWR200: New open access dataset of handwritten texts images in Russian,” in Computational Linguistics and Intellectual Technologies, 2023. Papers from the Annual Int. Conf. “Dialogue” (2023), Vol. 22, pp. 452–458. https://doi.org/10.28995/2075-7182-2023-22-452-458
A. Z. Broder, S. C. Glassman, M. S. Manasse, and G. Zweig, “Syntactic clustering of the Web,” Comput. Networks ISDN Syst. 29, 1157–1166 (1997). https://doi.org/10.1016/s0169-7552(97)00031-7
A. Z. Broder, “On the resemblance and containment of documents,” in Proc. Compression and Complexity of SEQUENCES 1997, Salerno, Italy, 1997 (IEEE Comput. Soc., 1997), pp. 21–29. https://doi.org/10.1109/sequen.1997.666900
U. Manber and G. Myers, “Suffix arrays: A new method for on-line string searches,” SIAM J. Comput. 22, 935–948 (2003). https://doi.org/10.1137/0222058
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).
U. Marti and H. Bunke, “The IAM-database: An English sentence database for offline handwriting recognition,” Int. J. Document Anal. Recognit. 5, 39–46 (2002). https://doi.org/10.1007/s100320200071
B. Gatos, G. Louloudis, T. Causer, K. Grint, V. Romero, J. A. Sanchez, A. H. Toselli, and E. Vidal, “Ground-truth production in the Transcriptorium project,” in 2014 11th IAPR International Workshop on Document Analysis Systems, Tours, France, 2014 (IEEE, 2014), pp. 237–241. https://doi.org/10.1109/das.2014.23
A. H. Toselli, V. Romero, M. Villegas, E. Vidal, and J. A. Sánchez, “HTR dataset ICFHR 2016 (1.2.0),” Zenodo (2016). https://doi.org/10.5281/zenodo.1297399
M. Potanin, D. Dimitrov, A. Shonenkov, V. Bataev, D. Karachev, M. Novopoltsev, and A. Chertok, “Digital Peter: New dataset, competition and handwriting recognition methods,” in The 6th Int. Workshop on Historical Document Imaging and Processing, Lausanne, Switzerland, 2021 (Association for Computing Machinery, New York, 2021), pp. 43–48. https://doi.org/10.1145/3476887.3476892
“School_notebooks,” (2021). https://github.com/ai-forever/htr_datasets/tree/main/school_notebooks
“IDP-forms,” (2021). https://github.com/ai-forever/htr_datasets/tree/main/IDP-forms
N. Toiganbayeva, M. Kasem, G. Abdimanap, K. Bostanbekov, A. Abdallah, A. Alimova, and D. Nurseitov, “KOHTD: Kazakh offline handwritten text dataset,” Signal Process.: Image Commun. 108, 116827 (2022). https://doi.org/10.1016/j.image.2022.116827
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The work is supported by the Innovation Promotion Fund, project no. 79068, request no. II-208298.
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Translated by E. Oborin
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Grabovoy, A.V., Kaprielova, M.S., Kildyakov, A.S. et al. Text Reuse Detection in Handwritten Documents. Dokl. Math. 108 (Suppl 2), S424–S433 (2023). https://doi.org/10.1134/S106456242370120X
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DOI: https://doi.org/10.1134/S106456242370120X