Automated Handwritten Equation Solver
Abstract
Mathematics has an important role in person’s life, so solving the mathematical equations is an essential. Solving mathematical expressions is not restricted to just students but also for mathematicians, physicists and scientists. Solving the mathematical equations is an interesting process.The traditional method of solving math expressions is unsatisfactory as the user should learn different rules and approaches for each mathematical equation. Also, these methods may take long time in complex or obscure problems which makes them subject to user errors and mistakes. The challenging in mathematical expressions must be written in a specific format, users prefer to write them on paper as an easy entering way than other computerized tools. This paper used the technology to introduce a new method over the traditional one using pen and paper. The equation handwriting easiness is blended (merge/integrate) with the advanced computer technologies speed to solve the equations with flexible robust way. An interface introduces that allows capturing the equations contained in an image then solving it without making the user dive into the complex rules. Various types of equations could be entered to this application (linear/nonlinear/quadratic) with achieving a convenient accuracy 95.7%.
References
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[2] L. Mafteiu-Scai, "Solving Equations Systems Using Artificial Intelligence - a Survey", International Journal of New Computer Architectures and their Applications, vol. 8, no. 3, pp. 102-118, 2018.
[3] S. Mori, C. Y. Suen, and K. Yamamoto, “Historical review of OCR research and development,” Proc. IEEE, vol. 80, no. 7, pp. 1029 –1058, 1992.
[4] S. Wei, X. Jin, and H. Li, “General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning,” Computational Mechanics, vol. 64, no. 5, pp. 1361–1374, 2019.
[5] S. Shinde and R. Waghulade, “Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network,” Communications on Applied Electronics, vol. 4, no. 7, pp. 1–6, 2016.
[6] Chavan, "Linear equation solver in Android using OCR", IOSR Journal of Engineering, vol. 03, no. 05, pp. 42-44, 2013.
[7] M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation". Journal of Electronic Imaging vol 13, no.1, pp. 146–165, 2004.
[8] H. Md Bipul, N. Feroza, J. Y.A., I. Md Zahidul, U. Md Jashim, “Recognition and Solution for Handwritten Equation Using Convolutional Neural Network”, International Conference on Informatics, Electronics & Vision and International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2018.
[9] H. Mouchere, C. Viard-Gaudin, R. Zanibbi, and U. Garain, “ICFHR2016 CROHME: Competition on Recognition of Online Handwritten Mathematical Expressions,” 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2016.
[10] E. Kussul and T. Baidyk, “Improved method of handwritten digit recognition tested on MNIST database”, Image Vis. Compute., vol. 22, no. 12, pp. 971–981, 2004.
[11] S. Shamim, M. Miah, A. Sarker, M. Rana and A. Jobair, "Handwritten Digit Recognition Using Machine Learning Algorithms", Indonesian Journal of Science and Technology, vol. 3, no. 1, p. 29, 2018.
[12] M. Adnan, F. Rahman, M. Imrul, N. AL and S. Shabnam, "Handwritten Bangla Character Recognition using Inception Convolutional Neural Network", International Journal of Computer Applications, vol. 181, no. 17, pp. 48-59, 2018.
[13] S. Sourav, S. Sudipta, C. Suhrid and S. M. Priya Ranjan, “A Machine Learning Framework for Recognizing Handwritten Digits Using Convexity-Based Feature Vector Encoding”, Kolkata, India, eHaCON, 2018.
[14] M. McDonnell, M. Tissera, T. Vladusich, A. van Schaik and J. Tapson, "Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm", PLOS ONE, vol. 10, no. 8, p. e0134254, 2015.
[15] C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling”, IEEE Trans. Pattern Anal. Mach. Intel., Vol. 35, no. 8, pp. 1915–1929, 2013.
[16] J. Pradeep, E. Srinivasan and S. Himavathi. “Neural Network based Handwritten Character Recognition system without feature extraction“, International Conference on Computer, Communication and Electrical Technology ICCCET, 2011.
[17] R. Jagan, R. A., “Recognition of Handwritten Characters using Deep Convolutional Neural Network,” International Journal of Innovative Technology and Exploring Engineering Special Issue, vol. 8, no. 6S4, pp. 314–317, 2019.
[18] P. Ravindra, “Training Deep Neural Networks”, Towards Data Science, Sept. 2018.
[19] R. Vaidya, D. Trivedi, S. Satra, M. Pimpale, “Handwritten Character Recognition Using Deep-Learning”. Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 772-775, 2018.
[20] R. Nijhawan, H. Sharma, H. Sahni, and A. Batra, “A deep learning hybrid CNN framework approach for vegetation cover mapping using deep features”, 13th International Conference on Signal Image Technology & Internet-Based Systems (SITIS), pp. 192-196, 2017.
[21] Y. Liang, J. Wang, S. Zhou, Y. Gong, and N. Zheng, “Incorporating image priors with deep convolutional neural networks for image super resolution”, Neuro computing, Vol. 194, pp. 340- 347, 2016.
[22] Y. LeCun, Y. Bengio and G. Hinton, "Deep learning", Nature, Vol. 521, pp. 436-444, 2015.
[23] Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in neural information processing systems, pp. 1097–1105, 2012.
[24] D. C. Ciresan, U. Meier, J. Masci, L. Maria Gambardella, and J. Schmidhuber, “Flexible, high performance convolutional neural networks for image classification”, Proceedings in 22nd International JointConference on Artificial Intelligence, Vol. 22, pp. 1237- 1242, 2011.
[25] M. Celik and B. Yanikoglu, “Handwritten mathematical formula recognition using a statistical approach,” 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU), 2011.
[26] N. E. Matsakis, Recognition of handwritten mathematical expressions. 1999.
[27] M. Rocklin and A. R. Terrel, “Symbolic Statistics with SymPy,” Computing in Science & Engineering, vol. 14, no. 3, pp. 88–93, 2012
Authors
Hussien, S. A. ., Azim, A. M. A. el ., & Hagag, A. . (2024). Automated Handwritten Equation Solver. International Journal of Advanced Science and Computer Applications, 4(1). https://doi.org/10.47679/ijasca.v4i1.60
This work is licensed under a Creative Commons Attribution 4.0 International License.