Abstract
Characters from various languages like Telugu consist of many strokes along with spatial relations among them. The representation for these strokes while being written on a mobile screen or any other personal computers needs to possess invariance to scaling, translation and if necessary, rotational aspects. An old technique for representing shapes has been explored and modified slightly to produce better results. The current work proposes that geometric moment-based features could be adapted to represent a stroke which possesses the needed invariance properties and the usage of neural networks to recognize the corresponding characters from the stroke combinations and the positional information of the strokes.
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
References
Viswanath, P., Gokaramaiah, T., Prabhakar Rao, G.V.: A Shape Representation Scheme for Hand-Drawn Symbol Representation, MIWAI 2011, LNAI, vol. 7080, pp. 213–224 (2011)
Prasanth, L., Babu, V., Sharma, R., Rao, G., Dinesh, M.: Elastic matching of online handwritten Tamil and Telugu scripts using local features. In: Ninth International Conference on Document Analysis and Recognition, 2007, ICDAR 2007, vol. 2, pp. 1028–1032. IEEE (2007)
Chaitanya, M.K.: Telugu vowels stroke data. https://github.com/krishr2d2/Telugu_Vowels_stroke_data (2017)
Agrawal, M., Bhaskarabhatla, A.S., Madhvanath, S.: Data collection for handwriting corpus creation in Indic scripts. In: International Conference on Speech and Language Technology and Oriental COCOSDA (ICSLT-COCOSDA 2004), New Delhi, India, November 2004
Moment (mathematics), Wikipedia. https://en.wikipedia.org/wiki/Moment_(mathematics)”
Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)
Flusser, J.: Moment invariants in image analysis. Proc. World Acad. Sci. Eng. Technol. 11(2), 196–201 (2006)
Tappert, C.C., Suen, C.Y., Wakahara, T.: The state of the art in online handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12(8), 787–808 (1990)
Swethalakshmi, H., Jayaraman, A., Chakravarthy, V.S., Sekhar, C.C.: Online handwritten character recognition of Devanagari and Telugu characters using support vector machines. In: Tenth International Workshop on Frontiers in Handwriting Recognition. Suvisoft (2006)
Artieres, T., Marukatat, S., Gallinari, P.: Online handwritten shape recognition using segmental hidden markov models. IEEE Trans. Pattern Anal. Mach. Intell. 29(2) (2007)
Acknowledgements
The work presented in this paper is partly funded by a DIC project with reference: DIC_Proj14_PV.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Movva, K.C., Pulabaigari, V. (2019). A Moment-Based Representation for Online Telugu Handwritten Character Recognition. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_3
Download citation
DOI: https://doi.org/10.1007/978-981-13-2514-4_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2513-7
Online ISBN: 978-981-13-2514-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)