Abstract
Hidden Markov Model (HMM) based offline cursive manually written word segmentation technique is proposed in this strategy. In this paper, we are utilizing a classification technique to perceive the written by hand word which is SVM. Dataset collection comprises handwritten words which are in the cursive configuration images are taken as input and these pictures comprise of noise and these noises are expelled by preprocessing strategy. The preprocessing technique incorporates word picture acquisition which is an RGB image; for additional steps, the RGB image is changed over to gray image. Later, thresholding is applied to the gray image. Thinning and skeletonization is connected to the thresholded image. At that point, noise is expelled from the manually written word image and a preprocessed binary matrix appears as a matrix. Over-segmented words are partitioned by potentially segmented column (PSC) and the HMM technique. At last, the character is perceived by utilizing SVM Method.
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
Al Hamad, H.A.: Over-segmentation of handwriting arabic scripts using an efficient heuristic technique. In: Wavelet Analysis and Pattern Recognition (ICWAPR), IEEE, pp. 180–185 (2012)
Jain, R., Doermann, D.: Writer identification using an alphabet of contour gradient descriptors. In: Document Analysis and Recognition (ICDAR), IEEE, pp. 550–554 (2013)
Eraqi, H.M., Abdelazeem, S.: A new efficient graphemes segmentation technique for offline arabic handwriting. In: Frontiers in Handwriting Recognition (ICFHR), IEEE, pp. 95–100 (2012)
Pant, A.K., Panday, S.P., Joshi, S.R.: Off-line Nepali handwritten character recognition using multilayer perception and radial basis function neural networks. In: Third Asian Himalayas International Conference. IEEE, pp. 1–5 (2012)
Bin, A.S.: UCOM Offline Dataset-An Urdu Handwritten Dataset Generation. Int. Arab J. Inf. Techno, 239–245 (2017)
Li, N., **e, X., Liu, W., Lam, K.M.: Combination of global and local baseline-independent features for offline Arabic handwriting recognition. In: 2012 21st International Conference Pattern Recognition (ICPR), IEEE, pp. 713–716 (2012)
Doetsch, P., Kozielski, M., Ney, H.: Fast and robust training of recurrent neural networks for offline handwriting recognition. In: 14th International Conference Frontiers in Handwriting Recognition (ICFHR), IEEE, pp. 279–284 (2014)
Putra, M.E.W., Supriana, I.: Structural offline handwriting character recognition using Levenshtein distance. In: 2015 International Conference Electrical Engineering and Informatics (ICEEI), IEEE, pp. 31–36 (2015)
Kumawat, P., Khatri, A., Nagaria, B.: Offline Handwriting recognition using invariant moments and curve let transform with combined SVM-HMM classifier. In: 2013 International Conference on Communication Systems and Network Technologies (CSNT), IEEE, pp. 144–148 (2013)
Shanjana, C., James, A.: Character segmentation in malayalam handwritten documents. In: International Conference on Advances in Engineering and Technology Research (ICAETR), IEEE, pp. 1–4 (2014)
Sagar, S., Dixit, S.: A comprehensive study on character segmentation. In: International Conference on ISMAC in Computational Vision and Bio-Engineering (ISMAC-CVB 2018)
Sagar, S., Dixit, S.: HMM segmentation approach for offline cursive handwritten words. Int. J. Eng. Sci. Comput. (IJESC) 8 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sagar, S., Dixit, S., Mahesh, B.V. (2020). Offline Cursive Handwritten Word Using Hidden Markov Model Technique. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 160. Springer, Singapore. https://doi.org/10.1007/978-981-32-9690-9_58
Download citation
DOI: https://doi.org/10.1007/978-981-32-9690-9_58
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9689-3
Online ISBN: 978-981-32-9690-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)