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OCR of Printed Mathematical Expressions

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Part of the book series: Advances in Pattern Recognition ((ACVPR))

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References

  1. Anderson, R.H. (1968). Syntax-directed recognition of hand-printed two-dimensional mathematics. Doctoral dissertation. Department of Engineering and Applied Physics, Harvard University.

    Google Scholar 

  2. Anderson, R.H. (1977). Two-dimensional mathematical notations. In: K.S. Fu, (Ed.). Syntactic Pattern Recognition Applications. New York: Springer, pp. 147-177.

    Google Scholar 

  3. Berman, B.P. and Fateman, R.J. (1994). Optical character recognition for typeset mathematics. ACM Proceedings of International Symposium on Sym-bolic and Algebraic Computation (ISSAC), Oxford, UK, pp. 348-353.

    Google Scholar 

  4. Blostein, D. and Grbavec, A. (1997). Recognition of mathematical notation. In: H. Bunke and P.S.P. Wang (Eds.). Handbook of Character Recognition and Document Image Analysis. Singapore: World Scientific, pp. 557-582.

    Google Scholar 

  5. Chan, K.-F. and Yeung, D.-Y. (2000). Mathematical expression recognition: a survey. International Journal on Document Analysis and Recognition, 3, pp. 3-15.

    Article  Google Scholar 

  6. Chan, K.-F. and Yeung, D.-Y. (2001). Error detection, error correction and performance evaluation in on-line mathematical expression recognition. Pat-tern Recognition, 34, pp. 1671-1684.

    Article  MATH  Google Scholar 

  7. Chang, S.-K. (1970). A method for the structural analysis of two-dimensional mathematical expressions. Information Sciences, 2, pp. 253-272.

    Article  MATH  Google Scholar 

  8. Chaudhuri, B.B. and Garain, U. (2000). An approach for recognition and interpretation of mathematical expressions in printed document. Pattern Analysis and Applications, 3, pp. 120-131.

    Article  Google Scholar 

  9. Chou, P.A. (1989). Recognition of equations using a two-dimensional stochas-tic context-free grammar. Proceedings of the SPIE, Visual Communication and Image Processing IV, 1199, pp. 852-863.

    Google Scholar 

  10. Chowdhury, S.P., Mandal, S., Das, A.K., and Chanda, B. (2003). Auto-mated segmentation of math-zones from document images. Proceedings of the Seventh International Conference Document Analysis and Recognition (ICDAR), Edinburgh, Scotland, pp. 755-759.

    Google Scholar 

  11. Eto, Y. and Suzuki, M. (2001). Mathematical formula recognition using vir-tual link network. Proceedings of the Sixth International Conference Document Analysis and Recognition (ICDAR), Seattle, USA, pp. 762-767.

    Google Scholar 

  12. Fateman, R.J. (1999). How to find mathematics on a scanned page. Proceed-ings of the SPIE, San Jose, California, USA, 3967, pp. 98-109.

    Google Scholar 

  13. Fateman, R.J. and Tokuyasu, T. (1996). Progress in recognizing typeset mathematics. Proceedings of the SPIE, San Jose, California, USA, 2660, pp. 7-50.

    Google Scholar 

  14. Fateman, R.J., Tokuyasu, T., Berman, B.P., and Mitchell, N. (1996). Optical character recognition and parsing of typeset mathematics. Journal of Visual Communication and Image Representation, 7, pp. 2-15.

    Article  Google Scholar 

  15. Garain, U. (2005). Recognition of printed and handwritten mathematical expressions. PhD thesis. Indian Statistical Institute.

    Google Scholar 

  16. Garain, U. and Chaudhuri, B.B. (2005). A corpus for OCR of printed mathe-matical expressions. International Journal of Document Analysis and Recognition (IJDAR), 7(4), pp. 241-259.

    Article  Google Scholar 

  17. Garain, U. and Chaudhuri, B.B. (2005). Segmentation of touching symbols for OCR of printed mathematical expressions: an approach based on mul-tifactorial analysis. Proceedings of the Eighth International Conference on Document Analysis and Recognition (ICDAR), Seoul, Korea, I, pp. 177-181.

    Google Scholar 

  18. Garain, U., Chaudhuri, B.B., and Ghosh, R.P. (2004). A multiple classifier system for recognition of printed mathematical symbols. The Seventeenth International Conference on Pattern Recognition (ICPR), Cambridge, UK, pp. 380-383.

    Google Scholar 

  19. Garain, U., Chaudhuri, B.B., and Ray Chaudhuri, A. (2004). Identification of embedded mathematical expressions in scanned documents. Seventeenth International Conference on Pattern Recognition (ICPR), Cambridge, UK, pp. 384-387.

    Google Scholar 

  20. Garcia, P. and Couasnon, B. (2002). Using a generic document recognition method for mathematical formulae recognition. In: D. Blostein and Y.-B. Kwon (Eds.). Proceedings of International Workshop on Graphics Recogni-tion (GREC) LNCS. Berlin, Heidelberg: Springer, 2390, pp. 236-244.

    Google Scholar 

  21. Grbavec, A. and Blostein, D. (1995). Mathematics recognition using graph rewriting. Proceedings of the Third International Conference on Document Analysis and Recognition (ICDAR), Montreal, Canada, pp. 417-421.

    Google Scholar 

  22. Ha, J., Haralick, R.M., and Phillips, I.T. (1995). Understanding mathemati-cal expressions from document images. Proceedings of the Third International Conference on Document Analysis and Recognition (ICDAR), Montreal, Canada, pp. 956-959.

    Google Scholar 

  23. Hull, J.F. (1996). Recognition of mathematics using a two-dimensional train-able context-free grammar. Master’s thesis. Department of Electrical Engi-neering and Computer Science, Massachusetts Institute of Technology.

    Google Scholar 

  24. Inoue, K., Miyazaki, R., and Suzuki, M. (1998). Optical recognition of printed mathematical documents. Proceedings of Asian Technology Conference in Mathematics (ATCM). New York: Springer, pp. 280-289.

    Google Scholar 

  25. Jin, J., Han, X., and Wang, Q. (2003). Mathematical formulas extraction. Proceedings of the Seventh International Conference Document Analysis and Recognition (ICDAR), Edinburgh, Scotland, pp. 1138-1141.

    Google Scholar 

  26. Kacem, A., Belaid, A., Ben Ahmed, M. (2001). Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context. International Journal on Document Analysis and Recognition (IJDAR), 4, pp. 97-108.

    Article  Google Scholar 

  27. Lavirotte, S. and Pottier, L. (1997). Optical formula recognition. Proceedings of the Fourth International Conference on Document Analysis and Recogni-tion (ICDAR), Ulm, Germany, pp. 357-361.

    Google Scholar 

  28. Lee, H.J. and Lee, M.C.(1994). Understanding mathematical expres-sions using procedure-oriented transformation. Pattern Recognition,27, pp. 447-457.

    Article  Google Scholar 

  29. Lee, H.J. and Wang, J.-S. (1997). Design of a mathematical expression un-derstanding system. Pattern Recognition Letters, 18, pp. 289-298.

    Article  Google Scholar 

  30. Miller, E.G. and Viola, P.A. (1998). Ambiguity and constraint in mathematical expression recognition. Proceedings of the National Conference of Artificial Intelligence. American Association of Artificial Intelligence, Madison, Wisconsin, pp. 784-791.

    Google Scholar 

  31. Nomura, A., Michishita, K., Uchida, S., and Suzuki, M. (2003). Detection and segmentation of touching characters in mathematical expressions. Proceed-ings of the Seventh International Conference Document Analysis and Recog-nition (ICDAR), Edinburgh, Scotland, pp. 126-130.

    Google Scholar 

  32. Okamoto, M., Imai, H., and Takagi, K. (2001). Performance evaluation of a robust method for mathematical expression recognition. Proceedings of the Sixth International Conference Document Analysis and Recognition (ICDAR), Seattle, USA, pp. 121-128.

    Google Scholar 

  33. Okamoto, M. and Miyazawa, A. (1992). An experimental implementation of document recognition system for papers containing mathematical expres-sions. In: H.S. Baird, H. Bunke, and Yamamoto (Eds.). Structured Document Image Analysis. New York: Springer, pp. 36-53.

    Google Scholar 

  34. Okamoto, M., Sakaguchi, S., and Suzuki, T. (1998). Segmentation of touching characters in formulae. Proceedings of the Third IAPR Workshop on Docu-ment Analysis Systems (DAS), Nagano, Japan, pp. 283-289.

    Google Scholar 

  35. Phillips, I. (1998). Methodologies for using UW databases for OCR and image understanding systems. Document Recognition V, Proceedings of the SPIE, San Jose, CA, USA, 3305, pp. 112-127.

    Google Scholar 

  36. Suzuki, M., Tamari, F., and Fukuda, R. (2003). INFTY - an integrated OCR system for mathematical documents. In: S. Uchida and T. Kanahori (Eds.). Proceedings of ACM Symposium on Document Engineering (DocEng), Grenoble, France, pp. 95-104.

    Google Scholar 

  37. Toumit, J.-Y., Garcia-Salicetti, S., and Emptoz, H. (1999). A hierarchi-cal and recursive model of mathematical expressions for automatic read-ing of mathematical documents. Proceedings of the Fifth International Conference Document Analysis and Recognition (ICDAR), Bangalore, India, pp. 119-122.

    Google Scholar 

  38. Twaakyondo, H.M. and Okamoto, M. (1995). Structure analysis and recog-nition of mathematical expressions. Proceedings of the Third International Conference on Document Analysis and Recognition (ICDAR), Montreal, Canada, pp. 430-437.

    Google Scholar 

  39. Uchida, S., Nomura, A., and Suzuki, M. (2005). Quantitative analysis of mathematical documents. International Journal on Document Analysis and Recognition (IJDAR), 7(4), pp. 211-218.

    Article  Google Scholar 

  40. Zanibbi, R., Blostein, D., and Cordy, J.R. (2002). Recognizing mathemat-ical expressions using tree transformation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, pp. 1455-1467.

    Article  Google Scholar 

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Garain, U., Chaudhuri, B.B. (2007). OCR of Printed Mathematical Expressions. In: Chaudhuri, B.B. (eds) Digital Document Processing. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84628-726-8_11

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  • DOI: https://doi.org/10.1007/978-1-84628-726-8_11

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-501-1

  • Online ISBN: 978-1-84628-726-8

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