Abstract
Machine learning tools have been extensively used for the prediction and classification of mathematical symbols, formulas, and expressions. Although the recognition and classification in handwritten text and scripts have reached a point of commensurate maturity, yet the recognition work related to mathematical symbols and expressions has remained a stimulating and challenging task throughout. So, in this work, we have used Weka, a machine learning tool, for the classification of handwritten mathematical symbols. The current literature witnesses a limited amount of research works for classification for handwritten mathematical text using this tool. We have endeavored to explore the potential classification rate of handwritten symbols while analyzing the performance by comparing the results obtained by several clustering, classification, regression, and other machine learning algorithms. The comparative analysis of 15 such algorithms has been performed, and the dataset used for the experiment incorporates selective handwritten math symbols. The experimental results output accuracy of 72.9215% using the Decision Table algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Afshan N, Afshar Alam M, Ali Mehdi S (2017) An analysis of mathematical expression recognition techniques. Int J Adv Res Comput Sci 8(5):2021–2026
MacLean S, Labahn G (2015) A Bayesian model for recognizing handwritten mathematical expressions. Pattern Recognit 48(8):2433–2445
Liu C-L, Nakashima K, Sako H, Fujisawa H (2003) Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognit 36(10):2271–2285
Shamim SM, Miah MBA, Angona Sarker MR, Al Jobair A (2018) Handwritten digit recognition using machine learning algorithms. Glob J Comput Sci Technol 18(1)
Plamondon R, Srihari SN (2000) Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84
Das N, Sarkar R, Basu S, Kundu M, Nasipuri M, Basu DK (2012) A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application. Appl Soft Comput 12(5):1592–1606
AlKhateeb JH, Pauplin O, Ren J, Jiang J (2011) Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition. knowl Syst 24(5):680–688
Tokas R, Bhadu A (2012) A comparative analysis of feature extraction techniques for handwritten character recognition. Int J Adv Technol Eng Res 2(4):215–219
Neves RFP, Lopes Filho ANG, Mello CAB, Zanchettin C (2011) A SVM based off-line handwritten digit recognizer. In: IEEE international conference on systems, man, and cybernetics, pp 510–515
Perwej Y, Chaturvedi A (2012) Machine recognition of hand written characters using neural networks. arXiv Prepr.arXiv1205.3964
Malon C, Uchida S, Suzuki M (2008) Mathematical symbol recognition with support vector machines. Pattern Recognit Lett 29(9):1326–1332
Obaidullah SM, Das N, Roy K (2014) An approach to distinguish machine printed and handwritten text from document images for indic script. Int J Appl Eng Res 9(20):4670–4675
Lin X, Gao L, Tang Z, Baker J, Sorge V (2014) Mathematical formula identification and performance evaluation in PDF documents. Int J Doc Anal Recognit 17(3):239–255
Shelke SV, Chandwadkar DM (2016) Handwritten Devnagri character recognition. GRD J Glob Res Dev J Eng 1(5):67–70
Lavanya K, Bajaj S, Tank P, Jain S (2017) Handwritten digit recognition using hoeffding tree, decision tree and random forests—a comparative approach. In: International conference on computational intelligence in data science (ICCIDS), pp 1–6
Nazemi A, Tavakolian N, Fitzpatrick D, Suen CY et al (2019) Offline handwritten mathematical symbol recognition utilising deep learning. arXiv Prepr.arXiv1910.07395
Jameel M, Shuja M, Mittal S (2020) Improved handwritten offline urdu characters recognition system using machine learning techniques. Int J Adv Sci Technol 29(6):4865–4978
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sakshi, Gautam, S., Sharma, C., Kukreja, V. (2021). Handwritten Mathematical Symbols Classification Using WEKA. In: Choudhary, A., Agrawal, A.P., Logeswaran, R., Unhelkar, B. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 778. Springer, Singapore. https://doi.org/10.1007/978-981-16-3067-5_4
Download citation
DOI: https://doi.org/10.1007/978-981-16-3067-5_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3066-8
Online ISBN: 978-981-16-3067-5
eBook Packages: Computer ScienceComputer Science (R0)