Abstract
In this paper, a robust word-level handwritten script identification technique has been proposed. A combination of shape based and texture based features are used to identify the script of the handwritten word images written in any of five scripts namely, Bangla, Devnagari, Malayalam, Telugu and Roman. An 87-element feature set is designed to evaluate the present script recognition technique. The technique has been tested on 3000 handwritten words in which each script contributes about 600 words. Based on the identification accuracies of multiple classifiers, Multi Layer Perceptron (MLP) has been chosen as the best classifier for the present work. For 5-fold cross validation and epoch size of 500, MLP classifier produces the best recognition accuracy of 91.79% which is quite impressive considering the shape variations of the said scripts.
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
Preview
Unable to display preview. Download preview PDF.
References
Chanda, S., Pal, S., Pal, U.: Word-wise Sinhala, Tamil and English Script Identification Using Gaussian Kernel SVM. IEEE (2008)
Chanda, S., Pal, S., Franke, K., Pal, U.: Two-stage Approach for Word-wise Script Identification. In: Proc. of 10th International Conference on Document Analysis and Recognition (ICDAR), pp. 926–930 (2009)
Patil, S.B., Subbareddy, N.V.: Neural network based system for script identification in Indian documents. Sadhana 27(pt.1), 83–97 (2002)
Pati, P.B., Ramakrishnan, A.G.: Word level multi-script identification. Pattern Recognition Letters 29, 1218–1229 (2008)
Dhanya, D., Ramakrishnan, A.G., Pati, P.B.: Script identification in printed bilingual documents. Sadhana 27(pt.1), 73–82 (2002)
Roy, K., Majumder, K.: Trilingual Script Separation of Handwritten Postal Document. In: Proc. of 6th Indian Conference on Computer Vision, Graphics & Image Processing, pp. 693–700 (2008)
Sarkar, R., Das, N., Basu, S., Kundu, M., Nasipuri, M., Basu, D.K.: Word level script Identification from Bangla and Devnagari Handwritten texts mixed with Roman scripts. Journal of Computing 2(2), 103–108 (2010)
Singh, P.K., Sarkar, R., Das, N., Basu, S., Nasipuri, M.: Identification of Devnagari and Roman scripts from multi-script handwritten documents. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds.) PReMI 2013. LNCS, vol. 8251, pp. 509–514. Springer, Heidelberg (2013)
Singh, P.K., Sarkar, R., Das, N., Basu, S., Nasipuri, M.: Statistical comparison of classifiers for script identification from multi-script handwritten documents. International Journal of Applied Pattern Recognititon 1(2), 152–172 (2014)
Zhang, D., Lu, G.: A comparative study of fourier descriptors for shape representation and retrieval. In: Proc. of 5th Asian Conference on Computer Vision (2002)
Kauppinen, H., Seppanen, T., Pietikainen, M.: An experimental comparison of auto-regressive and fourier-based descriptors in 2-D shape classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(2), 201–207 (1995)
Mingqiang, Y., Kidiyo, K., Joseph, R.: A survey of shape feature extraction techniques. In: Yin, P.-Y. (ed.) Pattern Recognition, pp. 43–90 (2008)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection, http://lear.inrialpes.fr
Ostu, N.: A thresholding selection method from gray-level histogram. IEEE Transaction on Systems Man Cybernet. SMC-8, 62–66 (1978)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, vol. I. Prentice-Hall, India (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Singh, P.K., Mondal, A., Bhowmik, S., Sarkar, R., Nasipuri, M. (2015). Word-Level Script Identification from Handwritten Multi-script Documents. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_62
Download citation
DOI: https://doi.org/10.1007/978-3-319-11933-5_62
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11932-8
Online ISBN: 978-3-319-11933-5
eBook Packages: EngineeringEngineering (R0)