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Direction and Semantic Features for Handwritten Balinese Character Recognition System

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Proceedings of Second International Conference on Electrical Systems, Technology and Information 2015 (ICESTI 2015)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 365))

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Abstract

This research presents a new work on feature extraction methods for handwritten and isolated Balinese character recognition system. Balinese script has different difficulties compared to other scripts and its uniqueness makes it very interesting yet challenging in the development of this system. The goal of this work is to achieve good performance on Balinese character recognition by comparing more than one feature extractor methods. Based on the experimentations, the direction and semantic features have shown better performance when both are paired as feature extractors. For the classifier, we employed Backpropagation Neural Network with 50 nodes in hidden layer and chosen the Lavenberg-Marquardt as the training algorithm. The overall performance accuracy is 90 %.

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References

  1. Sebastiano, I.: More than twenty years of advancements on frontiers in handwriting recognition. Pattern Recogn. 47(3), 916–928 (2014)

    Article  Google Scholar 

  2. Mantas, J.: An overview of character recognition methodologies. Pattern Recogn. 19(6), 425–430 (1986)

    Article  Google Scholar 

  3. Govindan, V.K., et al.: Character recognition—a review. Pattern Recogn. 23(7), 671–683 (1990)

    Article  Google Scholar 

  4. Cheng-Lin, L., Sako, H., Fujisawa, H.: Handwritten chinese character recognition: alternatives to nonlinear normalization. In: ICDAR (2003)

    Google Scholar 

  5. Hariharan, S., et al.: Online handwritten character recognition of devanagari and telugu characters using support vector machines. In: Tenth international workshop on Frontiers in handwriting recognition (2006)

    Google Scholar 

  6. Nawaz, S.N., et al.: An approach to offline arabic character recognition using neural networks. Electronics, circuits and systems, 2003. ICECS 2003. In: Proceedings of the 2003 10th IEEE international conference on. IEEE (2003)

    Google Scholar 

  7. Kawamura, A., et al.: Online recognition of freely handwritten japanese characters using directional feature densities. pattern recognition methodology and systems. In: 11th IAPR international conference on IEEE (1992)

    Google Scholar 

  8. Rudy A., et al., Feature Extraction for Java Character Recognition. Intelligence in the Era of Big Data, pp. 278–288. Springer, Berlin, (2015)

    Google Scholar 

  9. Budhi, G., et al.: Comparison of bidirectional associative memory, counter propagation and evolutionary neural network for java characters recognition. In: Advanced informatics: concept, theory and application (ICAICTA), (2014)

    Google Scholar 

  10. Luh P.A.: Offline balinese handwritten character recognition based on Backpropagation Neural Networks. In: Proceeding of national seminar on computer and intelligent system-KOMMIT 6, pp. 114–118, Bali (2010)

    Google Scholar 

  11. Kadek, A.D.I., Luh P.A.: Character segmentation for balinese script using canny edge detection algorithm. In: Proceedings of national conference of system & informatics, pp. 402–406, Bali (2011)

    Google Scholar 

  12. Luh, P.A.: Noise removal for ancient palm leaf manuscript of Bali. In: Proceedings of National Conference of Information System, KNSI, 1514–1518, Bali (2012)

    Google Scholar 

  13. Trier, Ø., et al.: Feature extraction methods for character recognition-a survey. Pattern Recogn. 29(4), 641–662 (1996)

    Article  Google Scholar 

  14. Zhen-Long B., et al.: A study on the use of 8-directional features for online handwritten chinese character recognition. In: Proceedings eighth international conference on IEEE (2005)

    Google Scholar 

  15. Kato Nei, et al.: A handwritten character recognition system using directional element feature and asymmetric mahalanobis distance. Pattern Anal. Mach. Intell., IEEE Trans. 21(3), 258–262 (1999)

    Google Scholar 

  16. Impedovo, D., Giuseppe, P.: Zoning methods for handwritten character recognition: a survey. Pattern Recogn. 47(3), 969–981 (2013)

    Article  Google Scholar 

  17. Phokharatkul, P., et al.: Off-Line hand written thai character recognition using ant-miner algorithm. Int. J. Comput. Control Quantum Inf. Eng. 1(8), (2007)

    Google Scholar 

  18. Hecht-Nielsen, R.: Theory of the Backpropagation Neural Network. In: IJCNN, International Joint Conference on Neural Networks (1989)

    Google Scholar 

  19. Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. Pws Pub, Boston (1996)

    Google Scholar 

  20. Eberhart, R.C. (ed.): Neural Network PC Tools: A Practical Guide. Academic Press (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. STMIK STIKOM Bali, Jl. Raya Puputan Renon no. 86, Bali, Indonesia

    Luh Putu Ayu Prapitasari & Komang Budiarta

Authors
  1. Luh Putu Ayu Prapitasari
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  2. Komang Budiarta
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Corresponding author

Correspondence to Luh Putu Ayu Prapitasari .

Editor information

Editors and Affiliations

  1. Electrical Engineering Department, Petra Christian University, Surabaya, Indonesia

    Felix Pasila

  2. Petra Christian University, Surabaya, Indonesia

    Yusak Tanoto

  3. Electrical Engineering Department, Petra Christian University, Surabaya, Indonesia

    Resmana Lim

  4. Electrical Engineering Department, Petra Christian University, Surabaya, Indonesia

    Murtiyanto Santoso

  5. University of Surabaya, Surabaya, Indonesia

    Nemuel Daniel Pah

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© 2016 Springer Science+Business Media Singapore

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Prapitasari, L.P.A., Budiarta, K. (2016). Direction and Semantic Features for Handwritten Balinese Character Recognition System. In: Pasila, F., Tanoto, Y., Lim, R., Santoso, M., Pah, N. (eds) Proceedings of Second International Conference on Electrical Systems, Technology and Information 2015 (ICESTI 2015). Lecture Notes in Electrical Engineering, vol 365. Springer, Singapore. https://doi.org/10.1007/978-981-287-988-2_15

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  • DOI: https://doi.org/10.1007/978-981-287-988-2_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-986-8

  • Online ISBN: 978-981-287-988-2

  • eBook Packages: EngineeringEngineering (R0)

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