Abstract
The detection of mathematical expression from PDF documents has been studied and advanced for recent years. In the process, the detection of variables of inline expressions that are represented by alphabetical characters is a challenge. Compared to other components of inline expressions, there are many factors that cause the ambiguities for the detection of variables. In this paper, the error in detecting variables in PDF scientific documents is analytically presented. Novel rules are proposed to improve the accuracy in the detection process. The experimental results on benchmark datasets containing English and Vietnamese documents show the effectiveness of the proposed method. The comparison with existing methods demonstrates the out-performance of the proposed method. Furthermore, pre-trained deep Convolutional Neural Networks are employed and optimized to automatically extract visual features of extracted components from PDF and machine learning algorithms are used to improve the accuracy of the detection.
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References
Zanibbi, R., Blostein, D.: Recognition and retrieval of mathematical expressions. Int. J. Doc. Anal. Recogn. 15, 331–357 (2012)
Britannica. https://www.britannica.com. Accessed 4 Sept 2018
Iwatsuki, K., et al.: Detecting in-line methematical expression in scientific documents. In: Proceedings of the 2017 ACM Symposium on Document Engineering, pp. 141–144. ACM (2017)
Yu, B., et al.: Extracting mathematical components directly from PDF documents for mathematical expression recognition and retrieval. In: ICSI, pp. 95–104. ACM (2014)
Suzuki, M., et al.: INFTY: an integrated OCR system for mathematical documents. In: Proceedings of the 2003 ACM Symposium on Document Engineering. ACM (2003)
Krizhevsky, A., et al.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1. IEEE (2012)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2016)
SVMlight. http://svmlight.joachims.org/. Accessed 4 Sept 2018
Friedman, J., Finke, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 2, 209–226 (1977)
Baker, J., et al.: Extracting precise data on the mathematical content of PDF documents. In: Proceedings of DML-08. Masaryk University Press (2008)
Rahman, F., Alam, H.: Conversion of PDF documents into HTML: a case study of document image analysis. In: Proceedings of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, pp. 87–91. IEEE (2003)
Poppler. https://poppler.freedesktop.org/. Accessed 4 Sept 2018
Magick. https://legacy.imagemagick.org/. Accessed 4 Sept 2018
PDFBox. https://pdfbox.apache.org/. Accessed 4 Sept 2018
Garain, U.: Identification of mathematical expressions in document images. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, pp. 1340–1344. IEEE (2009)
Jin, J., Han, X., Wang, Q.: Mathematical formulas detection. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition. IEEE (2003)
Gao, L., et al.: A deep learning-based formula detection method for PDF documents. In: Proceedings of the 2017 IEEE Conference on Document Analysis and Recognition. IEEE (2017)
Panyr, J.: Information retrieval techniques in rule-based expert systems. In: Bock, H.H., Ihm, P. (eds.) Classification, Data Analysis and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization, vol. 15, pp. 331–357. Springer, Heidelberg (1991). https://doi.org/10.1007/978-3-642-76307-6_26
ImageNet. http://www.image-net.org/. Accessed 4 Sept 2018
ACL. http://aclanthology.info/. Accessed 4 Sept 2018
Afzal, M.Z., et al.: DeepDocClassifier: document classification with deep convolutional neural network. In: 13th International Conference on Document Analysis and Recognition (ICDAR) (2015)
Acknowledgement
This work was supported by JST CREST Grant Number JPMJCR1513, Japan.
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Hai Phong, B., Manh Hoang, T., Le, TL., Aizawa, A. (2019). Mathematical Variable Detection in PDF Scientific Documents. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_60
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DOI: https://doi.org/10.1007/978-3-030-14802-7_60
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