IMEGE: Image-based Mathematical Expression Global Error

Mathematical expression recognition is an active research eld that is related to document image analysis and typesetting. Several approaches have been proposed to tackle this problem, and automatic methods for performance evaluation are required. Mathematical expressions are usually represented as a coded string like LATEX or MathML for evaluation purpose. This representation has ambiguity problems given that the same expression can be coded in several ways. For that reason, the proposed approaches in the past either manually analyzed recognition results or they reported partial errors as symbol error rate. In this study, we present a novel global performance evaluation measure for mathematical expression based on image matching. In this way, using an image representation solves the representation ambiguity as well as human beings do. The proposed evaluation method is a global error measure that also provides local information about the recognition result.Álvaro Muñoz, F.; Sánchez Peiró, JA.; Benedí Ruiz, JM. (2011). IMEGE: Image-based Mathematical Expression Global Error. http://hdl.handle.net/10251/1308

ICDAR 2021 competition on mathematical formula detection

[EN] This paper introduces the Competition on Mathematical Formula Detection that was organized for the ICDAR 2021. The main goal of this competition was to provide the researchers and practitioners a common framework to research on this topic. A large dataset was prepared for this contest where the GT was automatically generated and manually reviewed. Fourteen participants submitted their results for this competition and these results show that there is still room for improvement especially for the detection of embedded mathematical expressions.This work has been partially supported by the Ministerio de Ciencia y Tecnologia under the grant TIN2017-91452-EXP (IBEM) and by the Generalitat Valenciana under the grant PROMETEO/2019/121 (DeepPattern).Anitei, D.; Sánchez Peiró, JA.; Fuentes-López, JM.; Paredes Palacios, R.; Benedí Ruiz, JM. (2021). ICDAR 2021 competition on mathematical formula detection. Springer. 783-795. https://doi.org/10.1007/978-3-030-86337-1_52783795Deng, Y., Kanervisto, A., Rush, A.M.: What you get is what you see: a visual markup decompiler. arXiv abs/1609.04938 (2016)Gehrke, J., Ginsparg, P., Kleinberg, J.: Overview of the 2003 KDD cup. SIGKDD Explor. Newsl. (2), 149–151 (2003)Oberdiek, H.: The zref package. https://osl.ugr.es/CTAN/macros/latex/contrib/zref/zref.pdfLi, X., et al.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection (2020)Mahdavi, M., Zanibbi, R., MouchÚre, H., Viard-Gaudin, C., Garain, U.: ICDAR 2019 CROHME + TFD: competition on recognition of handwritten mathematical expressions and typeset formula detection. In: International Conference on Document Analysis and Recognition (2019)Ohyama, W., Suzuki, M., Uchida, S.: Detecting mathematical expressions in scientific document images using a U-Net trained on a diverse dataset. IEEE Access 7, 144030–144042 (2019)Phillips, I.: Methodologies for using UW databases for OCR and image understanding systems. In: Proceedings of the SPIE, Document Recognition V, vol. 3305, pp. 112–127 (1998)Pizzini, K., Bonzini, P., Meyering, J., Gordon, A.: GNUsed, a stream editor. https://www.gnu.org/software/sed/manual/sed.pdfSolovyev, R., Wang, W., Gabruseva, T.: Weighted boxes fusion: ensembling boxes from different object detection models. Image Vis. Comput. 107, 104117 (2021)Suzuki, M., Uchida, S., Nomura, A.: A ground-truthed mathematical character and symbol image database. In: Proceedings of the 8th International Conference on Document Analysis and Recognition (ICDAR 2005), pp. 675–679 (2005)Zanibbi, R., Blostein, D.: Recognition and retrieval of mathematical expressions. Int. J. Doc. Anal. Recogn. 14, 331–357 (2011)Zanibbi, R., Oard, D.W., Agarwal, A., Mansouri, B.: Overview of ARQMath 2020: CLEF lab on answer retrieval for questions on math. In: Arampatzis, A., et al. (eds.) CLEF 2020. LNCS, vol. 12260, pp. 169–193. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58219-7_1