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Local texture descriptors and projection histogram based handwritten Meitei Mayek character recognition

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Abstract

In this paper, we use the local texture descriptor and projection histogram feature for handwritten Meitei Mayek (Manipuri script) recognition. The different variations of the local binary pattern (LBP) namely, uniform LBP (ULBP), improved LBP (ILBP), center-symmetric LBP (CS-LBP) account for local texture descriptors. These features along with the projection histogram, separately and combined are presented to machine learning algorithms, k nearest neighbor (KNN), support vector machine (SVM) and Random Forest (RF) for classification of characters. The experiments by these feature descriptors with the classifiers have been evaluated on self-collected handwritten Meitei Mayek character dataset having 9800 samples. High accuracy is achieved even with the simple KNN classifier. Furthermore, classification with SVM and RF are explored, and the results are compared with the pixel-based methods which use the intensities value directly and a classic CNN model for recognition. The comparative results show that local texture descriptors and projection histogram strongly outperform pixel-based methods. The overall superior accuracy is achieved when the feature descriptors are combined with KNN classifier and performed even better than the CNN model.

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References

  1. Abdullah A, Veltkamp RC, Wiering M (2010) Fixed partitioning and salient points with MPEG-7 cluster correlograms for image categorization. Pattern Recogn 43 (3):650–662

    Article  Google Scholar 

  2. Baird HS (1987) The skew angle of printed documents. In: Proceedings of SPSE’s 40th annual conference and symposium on hybrid imaging systems, p 1987

  3. Blanz V, Schölkopf B, Bülthoff H, Burges C, Vapnik V, Vetter T (1996) Comparison of view-based object recognition algorithms using realistic 3D models. In: International conference on artificial neural networks. Springer, Berlin, pp 251–256

    Chapter  Google Scholar 

  4. Brink AA, Smit J, Bulacu ML, Schomaker LRB (2012) Writer identification using directional ink-trace width measurements. Pattern Recogn 45(1):162–171

    Article  Google Scholar 

  5. Burges CJC, Schölkopf B (1997) Improving the accuracy and speed of support vector machines. In: Advances in neural information processing systems, pp 375–381

  6. Chaithra D, Indira K (2017) Handwritten online character recognition for single stroke Kannada characters. In: 2017 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT). IEEE, pp 548–552

  7. Cortes C, Vapnik V (1995) Support-vector networks. Machine Learning 20 (3):273–297

    MATH  Google Scholar 

  8. Cover TM, Hart P (1967) Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1):21–27

    Article  Google Scholar 

  9. Dhanashree J, Pansare S (2015) Combination of multiple image features along with KNN classifier for classification of Marathi Barakhadi. In: 2015 international conference on computing communication control and automation. IEEE, pp 607–610

  10. Ghosh S, Barman U, Bora PK, Singh TH, Chaudhuri BB (2013) An OCR system for the Meetei Mayek script. In: 2013 fourth national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG). IEEE, pp 1–4

  11. Hangarge M, Santosh KC, Doddamani S, Pardeshi R (2013) Statistical texture features based handwritten and printed text classification in south indian documents. arXiv:1303.3087

  12. Hegadi RS (2013) Classification of kannada numerals using multi-layer neural network. In: Proceedings of international conference on advances in computing. Springer, New Delhi, pp 963–968

    Google Scholar 

  13. Inunganbi S, Choudhary P (2018) Recognition of handwritten Meitei Mayek script based on texture feature. Int J Nat Lang Comput.(IJNLC) 7(5):99–108

    Google Scholar 

  14. Inunganbi SC, Choudhary P (2018) Recognition of handwritten Meitei Mayek and english alphabets using combination of spatial features, Springer, Cham

  15. Kamble PM, Hegadi RS (2015) Handwritten Marathi character recognition using r-HOG feature. Procedia Comput Sci 45:266–274

    Article  Google Scholar 

  16. Kumar CJ, Kalita SK (2013) Recognition of handwritten numerals of Manipuri script. Int J Comput Appl 17:84

    Google Scholar 

  17. Laishram R, Singh AU, Singh NC, Singh AS, James H (2012) Simulation and modeling of handwritten Meitei Mayek digits using neural network approach. In: Proceedings of the Intl. Conf. on Advances in Electronics, Electrical and Computer Science Engineering-EEC, pp 355–358

  18. Likforman-Sulem L, Zahour A, Taconet B (2007) Text line segmentation of historical documents: a survey. International Journal of Document Analysis and Recognition (IJDAR) 9(2-4):123–138

    Article  Google Scholar 

  19. Manivannan A, Srinivasan H, Srihari S (2007) A statistical approach to line segmentation in handwritten documents. In: Document recognition and retrieval XIV. International society for optics and photonics, vol 6500, p 65000T

  20. Maring KA, Dhir R (2014) Recognition of cheising iyek/eeyek-manipuri digits using support vector machines. Ijcsit 2:1

    Article  Google Scholar 

  21. Nagy G, Seth S, Viswanathan M (1992) A prototype document image analysis system for technical journals. Computer 25(7):10–22

    Article  Google Scholar 

  22. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29 (1):51–59

    Article  Google Scholar 

  23. Olarik S, Schomaker L, Wiering M (2013) A comparison of feature and pixel-based methods for recognizing handwritten bangla digits. In: 2013 12th international conference on document analysis and recognition. IEEE, pp 165–169

  24. Partiningsih NDA, Fratama RR, Sari CA, Rachmawanto EH (2018) Handwriting ownership recognition using contrast enhancement and LBP feature extraction based on KNN. In: 2018 5th international conference on information technology, computer, and electrical engineering (ICITACEE). IEEE, pp 342– 346

  25. Rangachar K, Ogorman L, Govindaraju V (2002) Document image analysis: a primer. Sadhana 27(1): 3–22

    Article  Google Scholar 

  26. Santosh KC (2011) Character recognition based on dtw-radon. In: 2011 International conference on document analysis and recognition. IEEE, pp 264–268

  27. Santosh KC, Lamiroy B, Wendling L (2011) DTW for matching radon features: a pattern recognition and retrieval method. In: International conference on advanced concepts for intelligent vision systems. Springer, Berlin, pp 249–260

  28. Santosh KC, Lamiroy B, Wendling L (2013) DTW–Radon-based shape descriptor for pattern recognition. Int J Pattern Recognit Artif Intell 27(03):1350008

    Article  MathSciNet  Google Scholar 

  29. Santosh KC, Wendling L (2015) Character recognition based on non-linear multi-projection profiles measure. Frontiers of Computer Science 9(5):678–690

    Article  Google Scholar 

  30. Singh L, Singh PB, Singh TSD, Anilkumar S, Singh AU (2014) A neural network based handwritten Meitei Mayek alphabet optical character recognition system. In: 2014 IEEE international conference on computational intelligence and computing research. IEEE, pp 1–5

  31. Thokchom T, Bansal PK, Vig R, Bawa S (2010) Recognition of handwritten character of manipuri script. JCP 5(10):1570–1574

    Google Scholar 

  32. Vapnik V (2006) Estimation of dependences based on empirical data. Springer Science & Business Media, Berlin

    MATH  Google Scholar 

  33. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999

    Article  Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology Manipur, Imphal, India

    Sanasam Inunganbi & Khumanthem Manglem Singh

  2. National Institute of Technology, Hamirpur, Hamirpur, Himachal Pradesh, India

    Prakash Choudhary

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  1. Sanasam Inunganbi
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  2. Prakash Choudhary
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  3. Khumanthem Manglem Singh
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Correspondence to Sanasam Inunganbi.

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Inunganbi, S., Choudhary, P. & Singh, K.M. Local texture descriptors and projection histogram based handwritten Meitei Mayek character recognition. Multimed Tools Appl 79, 2813–2836 (2020). https://doi.org/10.1007/s11042-019-08482-4

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