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Fast Recognition of Asian Characters Based on Database Methodologies

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Data Management. Data, Data Everywhere (BNCOD 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4587))

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Abstract

Character recognition has been an active research area in the field of pattern recognition. The existing character recognition algorithms are focused mainly on increasing the recognition rate. However, as in the recent Google Library Project, the requirement for speeding up recognition of enormous amount of documents is growing. Moreover, the existing algorithms do not pay enough attention to Asian characters. In this paper, we propose an algorithm for fast recognition of Asian characters based on the database methodologies. Since the number of Asian characters is very large and their shapes are complicated, Asian characters require much more recognition time than numeric and Roman characters. The proposed algorithm extracts the feature from each of Asian characters through the Discrete Fourier Transform (DFT) and optimizes the recognition speed by storing and retrieving the features using a multidimensional index. We improve the recognition speed of the proposed algorithm using the association rule technique, which is a widely adopted data mining technique. The proposed algorithm has the advantage that it can be applied regardless of the language, size, and font of the characters to be recognized.

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References

  1. Agrawal, R., Faloutsos, C., Swami, A.N.: Efficient Similarity Search in Sequence Databases. In: Proc. Int’l Conf. on Foundations of Data Organization and Algorithms (FODO), Chicago, Illinois, pp. 69–84 (October 1993)

    Google Scholar 

  2. Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. Int’l Conf. on Management of Data, ACM SIGMOD, Washington, D.C. pp. 207–216 (May 1993)

    Google Scholar 

  3. Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. In: Proc. Int’l Conf. on Management of Data, ACM SIGMOD, pp. 322–331. Atlantic City, New Jersey (May 1990)

    Google Scholar 

  4. Belkasim, S.O., Shridhar, M., Ahmadi, M.: Pattern Recognition with Moment Invariants: a Comparative Study and New Results. Pattern Recognition 24(12), 1117–1138 (1991)

    Article  Google Scholar 

  5. Berchtold, S., Keim, D.A., Kriegel, H.-P.: The X-tree: An Index Structure for High-Dimensional Data. In: Proc. Int’l Conf. on Very Large Data Bases (VLDB), Mumbai, India, pp. 28–39 (September 1996)

    Google Scholar 

  6. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  7. Bunke, H., Wang, P.S.P.: Handbook of Character Recognition and Document Image Analysis. World Scientific Publishing Company, Singapore (1997)

    Google Scholar 

  8. Cho, W., Lee, S.-W., Kim, J.H.: Modeling and Recognition of Cursive Words with Hidden Markov Models. Pattern Recognition 28(12), 1941–1953 (1995)

    Article  Google Scholar 

  9. Google Book Search Library Project (2006), http://books.google.com/googleprint/library.html

  10. Halliday, D., Resnick, R., Walker, J.: Fundamentals of Physics, 7th edn. Wiley, Chichester (2004)

    Google Scholar 

  11. Kamel, I., Faloutsos, C.: On Packing R-trees. In: Proc. Int’l Conf. on Information and Knowledge Management (CIKM), Washington, D.C. pp. 490–499 (November 1993)

    Google Scholar 

  12. KS C 5601-1992, Code for Information Interchange (in Korean) (1992)

    Google Scholar 

  13. Mori, S., Nishida, H., Yamada, H.: Optical Character Recognition. Wiley, Chichester (1999)

    Google Scholar 

  14. Natsev, A., Rastogi, R., Shim, K.: WALRUS: A Similarity Retrieval Algorithm for Image Databases. IEEE Trans. Knowledge & Data Engineering (TKDE) 16(3), 301–316 (2004)

    Article  Google Scholar 

  15. Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)

    Google Scholar 

  16. Sim, D.-G., Ham, Y.-K., Park, R.-H.: On-Line Recognition of Cursive Korean Characters Using DP Matching and Fuzzy Concept. Pattern Recognition 27(12), 1605–1620 (1994)

    Article  Google Scholar 

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Richard Cooper Jessie Kennedy

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© 2007 Springer-Verlag Berlin Heidelberg

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Loh, WK., Park, YH., Yoon, YI. (2007). Fast Recognition of Asian Characters Based on Database Methodologies. In: Cooper, R., Kennedy, J. (eds) Data Management. Data, Data Everywhere. BNCOD 2007. Lecture Notes in Computer Science, vol 4587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73390-4_5

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  • DOI: https://doi.org/10.1007/978-3-540-73390-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73389-8

  • Online ISBN: 978-3-540-73390-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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