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Xu, Xinpeng; Tian, Xuedong; Yang, Fang

A retrieval and ranking method of mathematical documents based on CA-YOLOv5 and HFS. (English) Zbl 07607668

Math. Biosci. Eng. 19, No. 5, 4976-4990 (2022).

MSC:

68V99 Computer science support for mathematical research and practice

Keywords:

mathematical document retrieval; mathematical expressions; YOLOv5; HFS; CA

Software:

GitHub
Cite Review PDF
Full Text: DOI
OA License

References:

[1] W. Chu, F. Liu, Mathematical formula detection in heterogeneous document images, in <i>2013 Conference on Technologies and Applications of Artificial Intelligence</i>, (2013), 140-145. <a href=“https://doi.org/10.1109/TAAI.2013.38” target=“_blank”>https://doi.org/10.1109/TAAI.2013.38</a>
[2] P. Mali, P. Kukkadapu, M. Mahdavi, R. Zanibbi, ScanSSD: Scanning single shot detector for mathematical formulas in PDF document images, preprint, arXiv: 200308005.
[3] W, Detecting mathematical expressions in scientific document images using a U-Net trained on a diverse dataset, IEEE Access, 7, 144030-144042 (2019) · doi:10.1109/ACCESS.2019.2945825
[4] B. H. Phong, L. T. Dat, N. T. Yen, T. M. Hoang, T. L. Le, A deep learning based system for mathematical expression detection and recognition in document images, in <i>12th International Conference on Knowledge and Systems Engineering</i>, (2020), 85-90. <a href=“https://doi.org/10.1109/KSE50997.2020.9287693” target=“_blank”>https://doi.org/10.1109/KSE50997.2020.9287693</a>
[5] B. H. Phong, T. M. Hoang, T. L. Le, Mathematical variable detection based on convolutional neural network and support vector machine, in <i>2019 International Conference on Multimedia Analysis and Pattern Recognition</i>, (2019), 1-5. <a href=“https://doi.org/10.1109/MAPR.2019.8743543” target=“_blank”>https://doi.org/10.1109/MAPR.2019.8743543</a>
[6] X. Lin, L. Gao, Z. Tang, X. Lin, X. Hu, Mathematical formula identification in PDF documents, in <i>2011 International Conference on Document Analysis and Recognition</i>, (2011), 1419-1423. <a href=“https://doi.org/10.1109/ICDAR.2011.285” target=“_blank”>https://doi.org/10.1109/ICDAR.2011.285</a>
[7] X. Lin, L. Gao, Z. Tang, J. Baker, M. Alkalai, V. Sorge, A text line detection method for mathematical formula recognition, in <i>2013 12th International Conference on Document Analysis and Recognition</i>, (2013), 339-343. <a href=“https://doi.org/10.1109/ICDAR.2013.75” target=“_blank”>https://doi.org/10.1109/ICDAR.2013.75</a>
[8] X, Mathematical formula identification and performance evaluation in PDF documents, Int. J. Doc. Anal. Recog., 17, 239-255 (2013) · doi:10.1007/s10032-013-0216-1
[9] L. Gao, X. Yi, Y. Liao, Z. Jiang, Z. Yan, Z. Tang, A deep learning-based formula detection method for PDF documents, in <i>2017 14th IAPR International Conference on Document Analysis and Recognition</i>, (2017), 553-558. <a href=“https://doi.org/10.1109/ICDAR.2017.96” target=“_blank”>https://doi.org/10.1109/ICDAR.2017.96</a>
[10] B, Mathematical variable detection in PDF scientific documents, Intell. Inform. Database Syst., 11432, 694-706 (2019) · doi:10.1007/978-3-030-14802-7_60
[11] B, A hybrid method for mathematical expression detection in scientific document images, IEEE Access, 8, 83663-83684 (2020) · doi:10.1109/ACCESS.2020.2992067
[12] R, Learning to adaptively rank document retrieval system configurations, ACM Trans. Inform. Syst., 37, 1-41 (2019) · doi:10.1145/3231937
[13] K. Yamada, H. Murakami, Mathematical expression retrieval in PDFs from the Web using mathematical term queries, in <i>International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems</i>, <b>12144</b> (2020), 155-161. <a href=“https://doi.org/10.1007/978-3-030-55789-8_14” target=“_blank”>https://doi.org/10.1007/978-3-030-55789-8_14</a>
[14] P. Sojka, M. Růžička, V. Novotný, MIaS: math-aware retrieval in digital mathematical libraries, in <i>Proceedings of the 27th ACM International Conference on Information and Knowledge Management</i>, (2018), 1923-1926. <a href=“https://doi.org/10.1145/3269206.3269233” target=“_blank”>https://doi.org/10.1145/3269206.3269233</a>
[15] M, VMEXT: a visualization tool for mathematical expression trees, Intell. Comput. Math., 10383, 340-355 (2017) · Zbl 1364.68010 · doi:10.1007/978-3-319-62075-6
[16] M. Líška, P. Sojka, M. Růžička, Combining text and formula queries in math information retrieval, in <i>Proceedings of the First International Workshop on Novel Web Search Interfaces and Systems</i>, (2015), 7-9. <a href=“https://doi.org/10.1145/2810355.2810359” target=“_blank”>https://doi.org/10.1145/2810355.2810359</a>
[17] W, Accelerating substructure similarity search for formula retrieval, Adv. Inform. Retrieval, 12035, 714-727 (2020) · doi:10.1007/978-3-030-45439-5
[18] D, Math expression retrieval using an inverted index over symbol pairs, Int. Soc. Opt. Photonics, 9402, 940207 (2015) · doi:10.1117/12.2074084
[19] X, Retrieval of scientific documents based on HFS and BERT, IEEE Access, 9, 8708-8717 (2021) · doi:10.1109/ACCESS.2021.3049391
[20] S, Retrieval of mathematical information with syntactic and semantic structure over Web, J. Inform. Sci. Engineering, 36, 75-89 (2020)
[21] J, Computing similarity of Sci-Tech documents based on texts and formulas, Data Anal. Knowl. Discov., 2, 103-109 (2018) · doi:10.11925/infotech.2096-3467.2018.0211
[22] A, Context guided retrieval of math formulae from scientific documents, J. Inform. Optimization Sci., 40, 1559-1574 (2019) · doi:10.1080/02522667.2019.1703255
[23] P. Scharpf, M. Schubotz, A. Youssef, F. Hamborg, N. Meuschke, B. Gipp, Classification and clustering of arXiv documents, sections, and abstracts, comparing encodings of natural and mathematical language, in <i>Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020</i>, (2020), 137-146. <a href=“https://doi.org/10.1145/3383583.3398529” target=“_blank”>https://doi.org/10.1145/3383583.3398529</a>
[24] M, AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels, Int. Conf. Intell. Comput. Math., 237-250 (2020) · Zbl 1455.68269 · doi:10.1007/978-3-030-53518-6_15
[25] V, Hesitant fuzzy sets, Int. J. Intell. Syst., 25, 529-539 (2010) · Zbl 1198.03076 · doi:10.1002/int.20418
[26] G. Jocher, K. Nishimura, T. Mineeva, R. Vilariño: YOLOv5, 2020. Available from: <a href=“https://github.com/ultralytics/yolov5” target=“_blank”>https://github.com/ultralytics/yolov5</a>
[27] Q. Hou, D. Zhou, J. Feng, Coordinate attention for efficient mobile network design, in <i>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, (2021), 13713-13722. <a href=“https://doi.org/10.1109/CVPR46437.2021.01350” target=“_blank”>https://doi.org/10.1109/CVPR46437.2021.01350</a>
[28] J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in <i>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</i>, (2016), 779-788. <a href=“https://doi.org/10.1109/CVPR.2016.91” target=“_blank”>https://doi.org/10.1109/CVPR.2016.91</a>
[29] X, A mathematical indexing method based on the hierarchical features of operators in formulae, Adv. Eng. Res., 119, 49-52 (2017)
[30] M. Mahdavi, R. Zanibbi, H. Mouchere, C. Viard-Gaudin, U. Garain, ICDAR 2019 CROHME+ TFD: Competition on recognition of handwritten mathematical expressions and typeset formula detection, in <i>2019 International Conference on Document Analysis and Recognition</i>, (2019), 1533-1538. <a href=“https://doi.org/10.1109/ICDAR.2019.00247” target=“_blank”>https://doi.org/10.1109/ICDAR.2019.00247</a>
[31] C, A review of text similarity approaches, Inform. Sci., 37, 1007-7634 (2019) · doi:10.13833/j.issn.1007-7634.2019.03.026
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