Abstract
In this paper, the task of recognizing handwritten devanagari numerals by giving graph representation is introduced. Lipschitz embedding is explored to extract style, size invariant features from numeral graphs. Graph based features adequately model the cursivenes and are invariant to shape transformations. Recognition is carried out by SVM with radial basis function. Extensive experiments have been carried on standard dataset of CVPR ISI Kolkata. Comparative study of our results is presented with previous reported results on the dataset. From this study, graph representation seems to be robust and resilient.
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
Bajaj, R., Dey, L., Chaudhary, S.: Devanagiri numerals recognition by combining decision of multiple connectionist classifiers. Sadhana 27(1), 59–72 (2002)
Hanmandlu, M., Grover, J., Madasu, V.K., Vasikarla, S.: Input fuzzy modelling for the recognition of handwritten Hindi numerals. In: Proceedings of the International Conference Information Technology, pp. 208–213 (2007)
Ramteke, R.J., Mehrotta, S.C.: Feature extraction based on moment invariants for handwriting recognition. In: Proceedings of IEEE Conference, Cybernetics, Intelligent, System, pp. 1–6 (2006)
Banashree, N.P., Vasanta, R.: OCR for script identification of Hindi (devanagiri) numerals using feature sub selection by means of end-point with neuro-memetic model. Int. J. Intell. Technol. 2, 206–210
Bhattacharya, U., Parui, S.K., Shaw, B., Bhattacharya, K.: Neural combination of ANN and HMM for handwritten devanagiri numeral recognition. In: Lorette, G. (ed.) Tenth International Workshop on Frontiers in Handwriting Recognition, La Baule, October 2006
Patil, P.M., Sontakke, T.R.: Rotation, scale and translation invariant handwritten devanagiri numeral character recognition using general fuzzy neural network. Pattern Recogn. 40, 2110–2117 (2007)
Sharma, N., Pal, U., Kimura, F., Pal, S.: Recognition of off-line handwritten devnagari characters using quadratic classifier. In: Kalra, P.K., Peleg, S. (eds.) ICVGIP 2006. LNCS, vol. 4338, pp. 805–816. Springer, Heidelberg (2006). doi:10.1007/11949619_72
Chaudhary, B.B., Ghosh, R., Ghosh, M., Bhattacharya, U.: On recognition of handwritten devanagiri numerals. In: Proceedings of the Workshop on Learning Algorithms for Pattern Recognition (in Conjunction with the 18th Australian Joint Conference on Artificial Intelligence)
Rajashekhararadhya, S.V., Ranjan, P.V.: Efficient zone based feature extraction method for handwritten numeral recognition of four popular south Indian scripts. In: JATIT 2005–2008
Jayadevan, R., Satish, R., Patil, M., Pal, U.: Offline recognition of devanagiri script: a survey. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 41(6), 782–796 (2014)
Pal, U., Chaudhuri, B.B.: Indian script character recognition: a survey. Pattern Recogn. 37, 1887–1899 (2004)
Bag, S., Harit, G.: A survey on optical character recognition for Bengali and Hindi documents. In: Sadhana-Academy Proceedings in Engineering Science, vol. 38, no. I, pp. 1333–168. AS & Springer (2013)
Riesen, K., Bunke, H.: Graph Classification and Clustering Based on Vector Space Embedding: Series in Machine Perception Artificial Intelligence World Scientific, vol. 77. World Scientific Publishing Co., Inc. River Edge (2010)
Raveax, R., Abu-Aisheh, Z.: Graph for pattern recognition. University prancots-rabelais, October 2013
Bourgain, J.: On Lipchitz embedding of finite metric spaces in Hilbert spaces. Israel J. Math. 52, 46–52 (1985)
Riesen, K., Bunke, H.: Graph classification by means of Lipchitz embedding. IEEE Trans. Man Cybern. B Cybern. 39(6), 1472–1483 (2009)
Riesen, K., Bunke, H.: On Lipschitz embeddings of graphs. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008. LNCS (LNAI), vol. 5177, pp. 131–140. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85563-7_22
Gao, X., Xian, B., Tao, D.: A survey of graph edit distance. Pattern Anal. Appl. 13, 113–129 (2010)
Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27, 950–959 (2009)
Bhattacharya, U., Chaudhuri, B.B.: Databases for research on recognition of handwritten characters of Indian scripts. In: Proceedings of the 8th International Conference on Document Analysis and Recognition (ICDAR-2005), Seoul, Korea, vol. II, pp. 789–793 (2005)
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University (2003)
Das, N., Reddy, J.M., Sarkar, R., et al.: A statistical topological feature combination for recognition of handwritten numerals. Appl. Soft Comput. J. 12(8), 2486–2495 (2012)
Bhattacharya, U., Chaudhuri, B.B.: Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 444–457 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhat, M.I., Sharada, B. (2017). Recognition of Handwritten Devanagari Numerals by Graph Representation and Lipschitz Embedding. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_10
Download citation
DOI: https://doi.org/10.1007/978-981-10-4859-3_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4858-6
Online ISBN: 978-981-10-4859-3
eBook Packages: Computer ScienceComputer Science (R0)