Abstract
Mathematical expression or formula recognition system is evolving field in pattern recognition. Various methods are implemented by different researchers on wide areas. Handwritten mathematical expression field evolved in last decades rapidly. This paper aims to target and represent CNN as an application of the deep learning method for recognizing the handwritten mathematical symbols. The current trend of the deep learning architectures witnesses CNN is one of the most prominent and widely used techniques that have been successfully implemented in NLP, computer vision, and pattern recognition. The kinds of deep learning models have proven to produce a recognizable state of the art. In this paper, we recognize the handwritten mathematical symbols using CNN. This paper comprises stages involved in recognition process, challenges and proposed methodology used by the author to conduct their experiment and further result and future scope is discussed. Dataset used for this experiment is downloaded from the public available platform (HasyV2 Dataset 2018. A dataset of size 369 classes comprising 168,223 images has been used for experimentation, and the results relieve the accuracy of 76.17%. For further improvement, an extra dense net (fully connected layer) has been used. The DuosdenseNet model has been designed; the accuracy of recognizable results shows a decent work for consideration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
HasyV2 Dataset (2018)
Kukreja V, Dhiman P (2020) A deep neural network based disease detection scheme for Citrus fruits. In: 2020 International conference on smart electronics and communication (ICOSEC), pp 97–101
Chan KF, Yeung DY (2000) Mathematical expression recognition: a survey. Int J Doc Anal Recogn (iJDAR) 3(1):3–15. https://doi.org/10.1007/PL00013549
Hu L, Zanibbi R (2011) HMM-based recognition of online handwritten mathematical symbols using segmental K-means initialization and a modified pen-up/down feature. In: Proceedings of the international conference on document analysis and recognition, ICDAR, pp 457–462. https://doi.org/10.1109/ICDAR.2011.98
Shinde S, Waghulade R (2016) Handwritten mathematical expressions recognition using back propagation artificial neural network. Commun Appl Electron 4(7):1–6. https://doi.org/10.5120/cae2016652125
Huang Z, Dong M, Mao Q, Zhan Y (2014) Speech emotion recognition using CNN. In: Proceedings of the 22nd ACM international conference on multimedia, pp 801–804
Nguyen K, Fookes C, Ross A, Sridharan S (2017) Iris recognition with off-the-shelf CNN features: A deep learning perspective. IEEE Access 6:18848–18855
Hossain MB, Naznin F, Joarder YA, Zahidul Islam M, Uddin MJ (2018) Recognition and solution for handwritten equation using convolutional neural network. In: Proceedings Jt. 7th international conference informatics, electronics vision 2nd international conference imaging, vision pattern recognition, ICIEV-IVPR, pp 250–255. https://doi.org/10.1109/ICIEV.2018.8640991
Shuvo SN, Hasan F, Ahmed MU, Hossain SA, Abujar S (2020) MathNET: using CNN bangla handwritten digit, mathematical symbols, and trigonometric function recognition. In: Soft computing techniques and applications, Springer, pp 515–523
Julca-Aguilar FD, Hirata NST (2018) Symbol detection in online handwritten graphics using faster R-CNN. In: 2018 13th IAPR international workshop on document analysis systems (DAS), pp 151–156
Dai HAIN, Le Duc ANH, NAKAGAWA M (2014) Combination of LSTM and CNN for recognizing mathematical symbols. In: Proceedings of the 17th information-based induction sciences workshop
Drsouza L, Mascarenhas M (2018) Offline handwritten mathematical expression recognition using convolutional neural network. In: Proceedings of international conference on information, communication, engineering and technology, ICICET, pp 1–3. https://doi.org/10.1109/ICICET.2018.8533789
Nguyen CT, Khuong VTM, Nguyen HT, Nakagawa M (2020) CNN based spatial classification features for clustering offline handwritten mathematical expressions. Pattern Recognit Lett 131:113–120. https://doi.org/10.1016/j.patrec.2019.12.015
Kukreja V, Kumar D, Kaur A et al. (2020) GAN-based synthetic data augmentation for increased CNN performance in vehicle number plate recognition. In: 2020 4th international conference on electronics, communication and aerospace technology (ICECA), pp 1190–1195
Ciresan DC, Meier U, Gambardella LM, Schmidhuber J (2011) Convolutional neural network committees for handwritten character classification. In: 2011 International conference on document analysis and recognition, pp 1135–1139
Ahranjany SS, Razzazi F, Ghassemian MH (2010) A very high accuracy handwritten character recognition system for Farsi/Arabic digits using convolutional neural networks. In: 2010 IEEE fifth international conference on bio-inspired computing: theories and applications (BIC-TA), pp 1585–1592
Thoma M (2017) The hasyv2 dataset. arXiv Prepr. arXiv1701.08380
Hambal AM, Pei Z, Ishabailu FL (2017) Image noise reduction and filtering techniques. Int J Sci Res 6(3):2033–2038
Chauhan S, Sharma E, Doegar A et al. (2016) Binarization techniques for degraded document images—a review. In: 2016 5th international conference on reliability, infocom technologies and optimization (Trends and Future Directions)(ICRITO), pp 163–166
Zhang G, Ni G (2008) A new fast and effective recognition method based construction shape for printed digital. IJCSNS 8(5):309
Kaur D, Kaur Y (2014) Various image segmentation techniques: a review. Int J Comput Sci Mob Comput 3(5):809–814
Khokher MR, Ghafoor A, Siddiqui AM (2013) Image segmentation using multilevel graph cuts and graph development using fuzzy rule-based system. IET Image Process 7(3):201–211
Dey V, Zhang Y, Zhong M (2010) A review on image segmentation techniques with remote sensing perspective. vol 38. na Vienna, Austria
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sakshi, Sharma, C., Kukreja, V. (2022). CNN-Based Handwritten Mathematical Symbol Recognition Model. In: Tavares, J.M.R.S., Dutta, P., Dutta, S., Samanta, D. (eds) Cyber Intelligence and Information Retrieval. Lecture Notes in Networks and Systems, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-16-4284-5_35
Download citation
DOI: https://doi.org/10.1007/978-981-16-4284-5_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4283-8
Online ISBN: 978-981-16-4284-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)