Text Reuse Detection in Handwritten Documents

• Published in: Doklady. Mathematics Vol. 108; no. Suppl 2; pp. S424 - S433
• Main Authors: Grabovoy, A. V., Kaprielova, M. S., Kildyakov, A. S., Potyashin, I. O., Seyil, T. B., Finogeev, E. L., Chekhovich, Yu. V.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01-12-2023; Springer Nature B.V
• Subjects: Documents; Education; Handwriting recognition; Mathematics; Mathematics and Statistics; Pictures; Searching; handwriting; optical character recognition; handwritten text recognition; plagiarism detection; computer vision; text reuse detection
• ISSN: 1064-5624; 1531-8362
• DOI: 10.1134/S106456242370120X

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.