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Rejection measurement based on linear discriminant analysis for document recognition

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Abstract

In document recognition, it is often important to obtain high accuracy or reliability and to reject patterns that cannot be classified with high confidence. This is the case for applications such as the processing of financial documents in which errors can be very costly and therefore far less tolerable than rejections. This paper presents a new approach based on Linear Discriminant Analysis (LDA) to reject less reliable classifier outputs. To implement the rejection, which can be considered a two-class problem of accepting the classification result or otherwise, an LDA-based measurement is used to determine a new rejection threshold. This measurement (LDAM) is designed to take into consideration the confidence values of the classifier outputs and the relations between them, and it represents a more comprehensive measurement than traditional rejection measurements such as First Rank Measurement and First Two Ranks Measurement. Experiments are conducted on the CENPARMI database of numerals, the CENPARMI Arabic Isolated Numerals Database, and the numerals in the NIST Special Database 19. The results show that LDAM is more effective, and it can achieve a higher reliability while maintaining a high recognition rate on these databases of very different origins and sizes.

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References

  1. Lauer F., Suen C.Y., Bloch G.: A trainable feature extractor for handwritten digit recognition. Pattern Recognit. 40(6), 1816–1824 (2007)

    Article  MATH  Google Scholar 

  2. Zhang P., Bui T.D., Suen C.Y.: A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognit. 40(12), 3415–3429 (2007)

    Article  MATH  Google Scholar 

  3. Liu C.L., Nakashima K., Sako H., Fujisawa H.: Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recognit. 37(2), 265–279 (2004)

    Article  MATH  Google Scholar 

  4. Lam L., Suen C.Y.: Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Trans. Syst. Man Cybern. 27(5), 553–568 (1997)

    Article  Google Scholar 

  5. van Erp, M., Vuurpijl, L., Schomaker, L.: An overview and comparison of voting methods for pattern recognition. In: Proceedings 8th International Workshop on Frontiers in Handwriting Recognition (IWFHR’02), pp. 195–200 (2002)

  6. Brunelli R., Poggio T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993)

    Article  Google Scholar 

  7. Ventura, A.D., Schettini, R.: Graphic Symbol Recognition using a Signature Technique. In: Proceedings 12th International Conference Pattern Recognition (ICPR’94), vol. 2, pp. 533–535. Jerusalem, Israel (1994)

  8. Suda, P., Bridoux, C., Kammerer, B., Maderlechner, G.: Logo and word matching using a general approach to signal registration. In: Proceedings 4th International Conference Document Analysis and Recognition (ICDAR’97), pp. 61–65. Ulm, Germany (1997)

  9. Cao J., Ahmadi M., Shridhar M.: Recognition of handwritten numerals with multiple feature and multistage classifier. Pattern Recognit. 28(2), 153–160 (1995)

    Article  Google Scholar 

  10. Koerich, A.L.: Rejection strategies for handwritten word recognition. In: Proceedings 9th International Workshop on Frontiers in Handwriting Recognition (IWFHR’04), pp. 479–484 (2004)

  11. Pitrelli, J.F., Perrone, M.P.: Confidence-scoring post-processing for off-line handwritten-character recognition verification. In: Proceedings 7th International Conference on Document Analysis and Recognition (ICDAR’03) I, pp. 278–282 (2003)

  12. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. Software available at:. http://www.csie.ntu.edu.tw/~cjlin/libsvm (2001)

  13. Fisher R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179–188 (1936)

    Article  Google Scholar 

  14. Gao T.-F., Liu C.-L.: High accuracy handwritten Chinese character recognition using LDA-based compound distances. Pattern Recognit. 41(11), 3442–3451 (2008)

    Article  MATH  Google Scholar 

  15. Tang F., Tao H.: Fast linear discriminant analysis using binary bases. Pattern Recognit. Lett. 28(16), 2209–2218 (2007)

    Article  Google Scholar 

  16. Dong J.X., Krzyzak A., Suen C.Y.: Fast SVM training algorithm with decomposition on very large datasets. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 603–618 (2005)

    Article  Google Scholar 

  17. Suen C.Y., Nadal C., Legault R., Mai T.A., Lam L.: Computer recognition of unconstrained handwritten numerals. Proc. IEEE 80(7), 1162–1180 (1992)

    Article  Google Scholar 

  18. Alamri, H., Sadri, J., Suen, C.Y., Nobile, N.: A novel comprehensive database for Arabic off-Line handwriting recognition. In: Proceedings of the 11th International Conference on Frontiers in Handwriting Recognition (ICFHR 2008), pp. 664–669. Montreal, Canada (2008)

  19. Grother, P.J.: NIST Special Database 19 Handprinted Forms and Characters Database. NIST Gaithersburg, MD, USA

  20. He, C.L., Lam, L., Suen, C.Y.: A novel rejection measurement in handwritten numeral recognition based on Linear Discriminant Analysis. In: Proceedings of the 10th International Conference on Document Analysis and Recognition(ICDAR 2009), pp. 451–455. Barcelona, Spain (2009)

  21. Casey R.G.: Moment normalization of handprinted character. IBM J. Res. Dev. 14, 548–557 (1970)

    Article  MATH  Google Scholar 

  22. Otsu N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)

    Article  Google Scholar 

  23. Shi M., Fujisawa Y., Wakabayashi T., Kimura F.: Handwritten numeral recognition using gradient and curvature of gray scale image. Pattern Recognit. 35(10), 2051–2059 (2002)

    Article  MATH  Google Scholar 

  24. Pal, U., Wakabayashi, T., Kimura, F.: Comparative study of Devnagari handwritten character recognition using different feature and classifiers. In: Proceedings of the 10th International Conference on Document Analysis and Recognition (ICDAR 2009), pp. 1111–1115. Barcelona, Spain (2009)

  25. Vapnik V., Lerner A.: Pattern recognition using generalized portrait method. Autom. Remote Control 24, 774–780 (1963)

    Google Scholar 

  26. Abdleazeem S., El-Sherif E.: Arabic handwritten digit recognition. Int. J. Doc. Anal. Recognit. 11, 128–141 (2008)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, CENPARMI (Centre for Pattern Recognition and Machine Intelligence), Concordia University, Montreal, QC, H3G 1M8, Canada

    Chun Lei He, Louisa Lam & Ching Y. Suen

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  1. Chun Lei He
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  2. Louisa Lam
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  3. Ching Y. Suen
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Correspondence to Chun Lei He.

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He, C.L., Lam, L. & Suen, C.Y. Rejection measurement based on linear discriminant analysis for document recognition. IJDAR 14, 263–272 (2011). https://doi.org/10.1007/s10032-011-0154-8

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  • DOI: https://doi.org/10.1007/s10032-011-0154-8

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