Handwritten Mathematical Equations Conversion to LaTeX Equivalent

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© 2020 by IJCTT Journal
Volume-68 Issue-4
Year of Publication : 2020
Authors : Pooja Chaudhari, Bhagirath Prajapati, Priyanka Puvar
DOI :  10.14445/22312803/IJCTT-V68I4P138

How to Cite?

Pooja Chaudhari, Bhagirath Prajapati, Priyanka Puvar, "Handwritten Mathematical Equations Conversion to LaTeX Equivalent," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 248-252, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I4P138

Abstract
Recently in academics the usage of digital documents has been increasing so that it has been necessity to digitise most of the documents. With increasing usage of digital documents, demand of converting hand-written mathematical equations into digital form has been increased. Nowadays, LaTeX is popular for academic, scientific or technical articles. LaTeX is a tool for document formatting. LaTeX facilitates writing mathematical expression by remembering the syntax but it is difficult to remember syntax all the time. This problem can be solved with the concept of Deep Learning. Using which a model can be trained with a relative dataset then the trained model is used to detect mathematical expression which can further be converted into LaTeX syntax as required by the user.

Keywords
Mathematical expression, LaTeX, Neural networks, Deep Learning.

Reference
[1] Amit Schechter, Norah Borus, William Bakst. Converting Handwritten Mathematical Equations into LaTeX, CS221.
[2] M. Koschinski, H.-J. Winkler, M Lang, "Segmentation and Recognition of Symbols Within Handwritten Mathematical Expressions".
[3] H. Mouchère, C. Viard-Gaudin, R. Zanibbi and U. Garain, "ICFHR 2014 Competition on Recognition of On-Line Handwritten Mathematical Expressions (CROHME 2014)," 2014 14th International Conference on Frontiers in Handwriting Recognition, Heraklion, 2014.
[4] Ahmad-Montaser Awal, Harold Mouchère, Christian Viard-Gaudin. Towards Handwritten Mathematical Expression Recognition.
[5] https://medium.com/@curiousily/tensorflow-forhackers- part-iv-neural-network-from-scratch- 1a4f504dfa8
[6] http://neuralnetworksanddeeplearning.com
[7] https://www.simplilearn.com/neural-networks-tutorialarticle