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Statistical Relational Learning for Handwriting Recognition

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Inductive Logic Programming

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9046))

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Abstract

We introduce a novel application of handwriting recognition for Statistical Relational Learning. The proposed framework captures the intrinsic structure of handwriting by modeling fundamental character shape representations and their relationships using first-order logic. Our framework consists of three stages, (1) character extraction (2) feature generation and (3) class label prediction. In the character extraction stage, handwriting trajectory data is decoded into characters. Following this, character features (predicates) are defined across multiple levels - global, local and aggregated. Finally, a relational One-vs-All classifier is learned using relational functional gradient boosting (RFGB). We evaluate our approach on two datasets and demonstrate comparable accuracy to a well-established, meticulously engineered approach in the handwriting recognition paradigm.

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References

  1. Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)

    MATH  Google Scholar 

  2. Riedel, S., Chun, H.W., Takagi, T., Tsujii, J.: A Markov logic approach to bio-molecular event extraction. In: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task, Association for Computational Linguistics, pp. 41–49 (2009)

    Google Scholar 

  3. Poon, H., Vanderwende, L.: Joint inference for knowledge extraction from biomedical literature. In: ACL, Association for Computational Linguistics, pp. 813–821 (2010)

    Google Scholar 

  4. Yoshikawa, K., Riedel, S., Asahara, M., Matsumoto, Y.: Jointly identifying temporal relations with Markov logic. In: ACL, pp. 405–413 (2009)

    Google Scholar 

  5. Poon, H., Domingos, P.: Unsupervised semantic parsing. In: EMNLP, Association for Computational Linguistics, pp. 1–10 (2009)

    Google Scholar 

  6. Davis, J., Burnside, E.S., de Castro Dutra, I., Page, D., Ramakrishnan, R., Costa, V.S., Shavlik, J.W.: View learning for statistical relational learning: with an application to mammography. In: IJCAI, DTIC Document, pp. 677–683 (2005)

    Google Scholar 

  7. Davis, J., Ong, I.M., Struyf, J., Burnside, E.S., Page, D., Costa, V.S.: Change of representation for statistical relational learning. In: IJCAI, pp. 2719–2726 (2007)

    Google Scholar 

  8. Davis, J., Burnside, E., de Castro Dutra, I., Page, D.L., Santos Costa, V.: An integrated approach to learning Bayesian networks of rules. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 84–95. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Natarajan, S., Khot, T., Kersting, K., Gutmann, B., Shavlik, J.: Gradient-based boosting for statistical relational learning: the relational dependency network case. Mach. Learn. 86(1), 25–56 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  10. Weiss, J., Natarajan, S., Peissig, P., McCarty, C., Page, D.: Statistical relational learning to predict primary myocardial infarction from electronic health records. In: AI Magazine (2012)

    Google Scholar 

  11. Natarajan, S., Kersting, K., Ip, E., Jacobs, D., Carr, J.: Early prediction of coronary artery calcification levels using machine learning. In: Innovative Applications in AI (2013)

    Google Scholar 

  12. Natarajan, S., Saha, B., Joshi, S., Edwards, A., Khot, T., Davenport, E.M., Kersting, K., Whitlow, C.T., Maldjian, J.A.: Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain. Int. J. Mach. Learn. Cybern. 5(5), 659–669 (2013)

    Article  Google Scholar 

  13. Antanas, L., van Otterlo, M., Mogrovejo, O., Antonio, J., Tuytelaars, T., De Raedt, L.: A relational distance-based framework for hierarchical image understanding. In: Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, vol. 2, pp. 206–218 (2012)

    Google Scholar 

  14. Antanas, L., Hoffmann, M., Frasconi, P., Tuytelaars, T., De Raedt, L.: A relational kernel-based approach to scene classification. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 133–139, IEEE (2013)

    Google Scholar 

  15. Riedel, S., McClosky, D., Surdeanu, M., McCallum, A., Manning, C.D.: Model combination for event extraction in bionlp 2011. In: Proceedings of the BioNLP Shared Task 2011 Workshop, Association for Computational Linguistics, pp. 51–55 (2011)

    Google Scholar 

  16. Dietterich, T.G.: Machine learning for sequential data: a review. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 15–30. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  17. Graves, A., Liwicki, M., Bunke, H., Schmidhuber, J., Fernández, S.: Unconstrained on-line handwriting recognition with recurrent neural networks. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) Advances in Neural Information Processing Systems 20, pp. 577–584. MIT Press, Cambridge (2007)

    Google Scholar 

  18. Liwicki, M., Bunke, H., et al.: HMM-based on-line recognition of handwritten whiteboard notes. In: Tenth International Workshop on Frontiers in Handwriting Recognition (2006)

    Google Scholar 

  19. Shivram, A., Zhu, B., Setlur, S., Nakagawa, M., Govindaraju, V.: Segmentation based online word recognition: a conditional random field driven beam search strategy. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 852–856, IEEE (2013)

    Google Scholar 

  20. Guberman, S.A., Lossev, I., Pashintsev, A.V.: Method and apparatus for recognizing cursive writing from sequential input information, 17 May 1994. US Patent 5,313,527

    Google Scholar 

  21. Li, X., Yeung, D.Y.: On-line handwritten alphanumeric character recognition using dominant points in strokes. Pattern Recogn. 30(1), 31–44 (1997)

    Article  Google Scholar 

  22. Plamondon, R., Maarse, F.J.: An evaluation of motor models of handwriting. IEEE Trans. Syst. Man Cybern. 19(5), 1060–1072 (1989)

    Article  Google Scholar 

  23. Parizeau, M., Plamondon, R.: A fuzzy-syntactic approach to allograph modeling for cursive script recognition. IEEE Trans. Pattern Anal. Mach. Intell. 17(7), 702–712 (1995)

    Article  Google Scholar 

  24. Malaviya, A., Peters, L.: Fuzzy handwriting description language: Fohdel. Pattern Recogn. 33(1), 119–131 (2000)

    Article  Google Scholar 

  25. Ziino, D., Amin, A., Sammut, C.: Recognition of hand printed Latin characters using machine learning. In: Proceedings of the Third International Conference on Document Analysis and Recognition 1995, vol. 2, pp. 1098–1102, IEEE (1995)

    Google Scholar 

  26. Amin, A., Sammut, C., Sum, K.: Learning to recognize hand-printed Chinese characters using inductive logic programming. Int. J. Pattern Recogn. Artif. Intell. 10(07), 829–847 (1996)

    Article  Google Scholar 

  27. Manke, S., Finke, M., Waibel, A.: NPen++: a writer independent, large vocabulary on-line cursive handwriting recognition system. In: Proceedings of the Third International Conference on Document Analysis and Recognition 1995, vol. 1, pp. 403–408, August 1995

    Google Scholar 

  28. Neville, J., Jensen, D.: Relational dependency networks. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)

    Google Scholar 

  29. Domingos, P., Lowd, D.: Markov logic: an interface layer for artificial intelligence. Synth. Lect. Artif. Intell. Mach. Learn. 3(1), 1–155 (2009)

    Article  MATH  Google Scholar 

  30. Kersting, K., De Raedt, L.: Bayesian logic programming: theory and tool. In: Getoor, L., Taskar, B. (eds.) Statistical Relational Learning, p. 291. MIT Press, Cambridge (2007)

    Google Scholar 

  31. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: IJCAI, vol. 99, pp. 1300–1309 (1999)

    Google Scholar 

  32. Singla, P., Domingos, P.: Discriminative training of Markov logic networks. In: AAAI, vol. 5, pp. 868–873 (2005)

    Google Scholar 

  33. Lowd, D., Domingos, P.: Recursive random fields. In: IJCAI, pp. 950–955 (2007)

    Google Scholar 

  34. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  35. Connell, S.D.: Online handwriting recognition using multiple pattern class models. Ph.D. thesis (2000)

    Google Scholar 

  36. Malaviya, A., Peters, L.: Fuzzy feature description of handwriting patterns. Pattern Recogn. 30(10), 1591–1604 (1997)

    Article  MATH  Google Scholar 

  37. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recogn. 44(8), 1761–1776 (2011)

    Article  Google Scholar 

  38. Shivram, A., Ramaiah, C., Setlur, S., Govindaraju, V.: IBM_UB_1: a dual mode unconstrained English handwriting dataset. In: Proceedings of the Twelfth International Conference on Document Analysis and Recognition 2013, pp. 13–17, (2013)

    Google Scholar 

  39. Guyon, I., Schomaker, L., Plamondon, R., Liberman, M., Janet, S.: Unipen project of on-line data exchange and recognizer benchmarks. In: Proceedings of the 12th IAPR International. Conference on Pattern Recognition 1994. Vol. 2-Conference B: Computer Vision &; Image Processing, vol. 2, pp. 29–33, IEEE (1994)

    Google Scholar 

  40. Liu, C.L., Sako, H., Fujisawa, H.: Performance evaluation of pattern classifiers for handwritten character recognition. Int. J. Doc. Anal. Recogn. 4(3), 191–204 (2002)

    Article  Google Scholar 

  41. Zhu, B., Shivram, A., Setlur, S., Govindaraju, V., Nakagawa, M.: Online handwritten cursive word recognition using segmentation-free MRF in combination with P2DBMN-MQDF. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 349–353, IEEE (2013)

    Google Scholar 

  42. Cao, J., Shridhar, M., Kimura, F., Ahmadi, M.: Statistical and neural classification of handwritten numerals: a comparative study. In: 11th IAPR International Conference on Pattern Recognition 1992, vol. II. Conference B: Pattern Recognition Methodology and Systems, Proceedings, pp. 643–646, IEEE (1992)

    Google Scholar 

  43. SNNS toolkit. http://www.ra.cs.uni-tuebingen.de/SNNS/

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Author information

Authors and Affiliations

  1. University at Buffalo - SUNY, Buffalo, USA

    Arti Shivram & Venu Govindaraju

  2. University of Wisconsin-Madison, Madison, USA

    Tushar Khot

  3. Indiana University, Bloomington, USA

    Sriraam Natarajan

Authors
  1. Arti Shivram
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  2. Tushar Khot
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  3. Sriraam Natarajan
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  4. Venu Govindaraju
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Corresponding author

Correspondence to Arti Shivram .

Editor information

Editors and Affiliations

  1. Department of Computer Science, KU Leuven, Leuven, Belgium

    Jesse Davis

  2. Department of Computer Science, KU Leuven, Leuven, Belgium

    Jan Ramon

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Shivram, A., Khot, T., Natarajan, S., Govindaraju, V. (2015). Statistical Relational Learning for Handwriting Recognition. In: Davis, J., Ramon, J. (eds) Inductive Logic Programming. Lecture Notes in Computer Science(), vol 9046. Springer, Cham. https://doi.org/10.1007/978-3-319-23708-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-23708-4_9

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