Markov models for offline handwriting recognition: a survey

Open Access
Full Paper

Abstract

Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic offline handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. It is therefore the goal of this survey to provide a comprehensive overview of the application of Markov models in the research field of offline handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, we will introduce the typical architecture of a Markov-model-based offline handwriting recognition system and make the reader familiar with the essential theoretical concepts behind Markovian models. Then, we will give a thorough review of the solutions proposed in the literature for the open problems how to apply Markov-model-based approaches to automatic offline handwriting recognition.

Keywords

Offline handwriting recognition Hidden Markov models n-Gram language models 

References

  1. 1.
    Arica N., Yarman-Vural F.T.: One-dimensional representation of two-dimensional information for HMM based handwriting recognition. Pattern Recogn. Lett. 21, 583–592 (2000)CrossRefGoogle Scholar
  2. 2.
    Arica N., Yarman-Vural F.T.: An overview of character recognition focused on off-line handwriting. IEEE Trans. Syst. Man Cybern. C Appl. 31(2), 216–232 (2001)CrossRefGoogle Scholar
  3. 3.
    Arica N., Yarman-Vural F.T.: Optical character recognition for cursive handwriting. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 801–813 (2002)CrossRefGoogle Scholar
  4. 4.
    Austin, S., Schwartz, R., Placeway, P.: The forward-backward search algorithm. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 697–700. Toronto (1991)Google Scholar
  5. 5.
    Baum L., Petrie T.: Statistical inference for probabilistic functions of finite state markov chains. Ann. Math. Stat. 37, 1554–1563 (1966)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Baum L., Petrie T., Soules G., Weiss N.: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat. 41, 164–171 (1970)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Benouareth, A., Ennaji, A., Sellami, M.: Semicontinuous HMMs with explicit state duration applied to Arabic handwritten word recognition. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 97–102. La Baule, France (2006)Google Scholar
  8. 8.
    Bertolami, R., Bunke, H.: Multiple handwritten text line recognition systems derived from specific integration of a language model. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 1, pp. 521–525. Seoul, Korea (2005)Google Scholar
  9. 9.
    Bertolami, R., Uchida, S., Zimmermann, M., Bunke, H.: Non-uniform slant correction for handwritten text line recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 1, pp. 18–22. Curitiba, Brazil (2007)Google Scholar
  10. 10.
    Besner, D., Humphreys, G.W. (eds.): Basic Processes in Reading: Visual Word Recognition. Lawrence Earlbaum Associates, Hillsdale (1991)Google Scholar
  11. 11.
    Bilmes, J.: What HMMs can’t do: a graphical model perspective. In: Beyond HMM: Workshop on Statistical Modeling Approach for Speech Recognition. Kyoto, Japan (2004). ATR Invited Paper and LectureGoogle Scholar
  12. 12.
    Bocchieri E., Mak B.K.W.: Subspace distribution clustering hidden Markov model. IEEE Trans. Speech Audio Process. 9(2), 264–275 (2001)CrossRefGoogle Scholar
  13. 13.
    Bozinovic R.M., Srihari S.N.: Off-line cursive script word recognition. IEEE Trans. Pattern Anal. Mach. Intell. 11(1), 69–83 (1989)CrossRefGoogle Scholar
  14. 14.
    Brakensiek, A., Rigoll, G.: A comparison of character n-grams and dictionaries used for script recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 241–245. Seattle (2001)Google Scholar
  15. 15.
    Brakensiek, A., Rigoll, G.: Combination of multiple classifiers for handwritten word recognition. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 117–122. Niagara on the Lake, Canada (2002)Google Scholar
  16. 16.
    Brakensiek A., Rigoll G.: Handwritten address recognition using hidden Markov models. In: Dengel, A., Junker, M., Weisbecker, A. (eds) Reading and Learning—Adaptive Content Recognition, Lecture Notes in Computer Science, vol. 2956, pp. 103–122. Springer, Berlin (2004)Google Scholar
  17. 17.
    Brakensiek, A., Rottland, J., Rigoll, G.: Handwritten address recognition with open vocabulary using character n-grams. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 357–362. Niagara on the Lake, Canada (2002)Google Scholar
  18. 18.
    Brakensiek, A., Rottland, J., Rigoll, G.: Confidence measures for an address reading system. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 1, pp. 294–298. Edinburgh (2003)Google Scholar
  19. 19.
    Brakensiek, A., Rottland, J., Wallhoff, F., Rigoll, G.: Adaptation of an address reading system to local mail streams. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 872–876. Seattle (2001)Google Scholar
  20. 20.
    Brakensiek, A., Willett, D., Rigoll, G.: Improved degraded document recognition with hybrid modeling techniques and character n-grams. In: Proceedings of the International Conference on Pattern Recognition, vol. 4, pp. 438–441. Barcelona (2000)Google Scholar
  21. 21.
    Britto, A.D.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.: A two-stage HMM-based system for recognizing handwritten numeral strings. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 396–400. Seattle (2001)Google Scholar
  22. 22.
    Bunke, H.: Recognition of cursive Roman handwriting—Past, present and future. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 1, pp. 448–459 (2003)Google Scholar
  23. 23.
    Bunke H., Roth M., Schukat-Talamazzini E.G.: Off-line cursive handwriting recognition using hidden Markov models. Pattern Recogn. 9(9), 1399–1413 (1995)CrossRefGoogle Scholar
  24. 24.
    Caesar, T., Gloger, J.M., Mandler, E.: Preprocessing and feature extraction for a handwriting recognition system. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 408–411. Tsukuba Science City, Japan (1993)Google Scholar
  25. 25.
    Cai, J., Liu, Z.Q.: Markov random field models for handwritten word recognition. In: Proceedings of the International Conference Intelligent Processing Systems (ICIPS), vol. 2, pp. 1400–1404. IEEE, Beijing (1997)Google Scholar
  26. 26.
    Cai J., Liu Z.Q.: Off-line unconstrained handwritten word recognition. Int. J. Pattern Recogn. Artif. Intell. 14(3), 259–280 (2000)CrossRefGoogle Scholar
  27. 27.
    Chen S.F., Goodman J.: An empirical study of smoothing techniques for language modeling. Comput. Speech Lang. 13, 359–394 (1999)CrossRefGoogle Scholar
  28. 28.
    Cho W., Lee S.W., Kim J.H.: Modeling and recognition of cursive words with hidden Markov models. Pattern Recogn. 28(12), 1941–1953 (1995)CrossRefGoogle Scholar
  29. 29.
    Choisy, C.: Dynamic handwritten keyword spotting based on the NSHP-HMM. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 1, pp. 242–246. Curitiba, Brazil (2007)Google Scholar
  30. 30.
    Coetzer J., Herbst B.M., du Preez J.A.: Offline signature verification using the discrete Radon transform and a hidden Markov models. EURASIP J. Appl. Signal Process. 4, 559–571 (2004)Google Scholar
  31. 31.
    Coetzer, J., Herbst, B.M., du Preez, J.A.: Off-line signature verification: a comparison between human and machine performance. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 481–486. La Baule, France (2006)Google Scholar
  32. 32.
    Colthurst, T., Kimball, O., Richardson, F., Shu, H., Wooters, C., Iyer, R., Gish, H.: The 2000 BBN Byblos LVCSR system. In: 2000 Speech Transcription Workshop. Maryland (2000)Google Scholar
  33. 33.
    Daniels, P.T., Bright, W. (eds.): The World’s Writing Systems. Oxford University Press, New York (1996)Google Scholar
  34. 34.
    Davis R.: Magic paper: sketch-understanding research. IEEE Comput. 40(9), 34–41 (2007)Google Scholar
  35. 35.
    Decerbo, M., MacRostie, E., Natarajan, P.: The BBN Byblos Pashto OCR system. In: Proceedings of the 1st ACM Workshop on Hardcopy Document Processing, pp. 29–32. ACM New York, NY, USA, Washington, DC, USA (2004)Google Scholar
  36. 36.
    Dehghan, M., Faez, K., Ahmadi, M., Shridhar, M.: Off-line unconstrained Farsi handwritten word recognition using fuzzy vector quantization and hidden Markov word models. In: Proceedings of the International Conference on Pattern Recognition, vol. 2, pp. 351–354. Barcelona (2000)Google Scholar
  37. 37.
    Dempster A.P., Laird N.M., Rubin D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. Ser. B 39(1), 1–22 (1977)MATHMathSciNetGoogle Scholar
  38. 38.
    Ding, Y., Kimura, F., Miyake, Y., Shridhar, M.: Accuracy improvement of slant estimation for handwritten words. In: Proceedings of the International Conference on Pattern Recognition, vol. 4, pp. 527–530. Barcelona (2000)Google Scholar
  39. 39.
    Duda R.O., Hart P.E., Stork D.G.: Pattern Classification. 2nd edn. Wiley Interscience, New York (2000)Google Scholar
  40. 40.
    El Abed, H., Märgner, V.: Comparison of different preprocessing and feature extraction methods for offline recognition of handwritten Arabic words. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 974–978. Curitiba, Brazil (2007)Google Scholar
  41. 41.
    El-Hajj, R., Likforman-Sulem, L., Mokbel, C.: Arabic handwriting recognition using baseline dependant features and hidden Markov modeling. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 893–897. Seoul, Korea (2005)Google Scholar
  42. 42.
    El-Hajj, R., Likforman-Sulem, L., Mokbel, C.: Combining slanted-frame classifiers for improved HMM-based Arabic handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(7) (2009)Google Scholar
  43. 43.
    El-Hajj, R., Mokbel, C., Likforman-Sulem, L.: Combination of HMM-based classifiers for recognition of Arabic handwritten words. In: Proceedings of the Internatinal Conference on Document Analysis and Recognition, vol. 2, pp. 959–963. Curitiba, Brazil (2007)Google Scholar
  44. 44.
    El-Yacoubi A., Gilloux M., Sabourin R., Suen C.Y.: An HMM-based approach for off-line unconstrained handwritten word modeling and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 752–760 (1999)CrossRefGoogle Scholar
  45. 45.
    Feng, B., Ding, X., Wu, Y.: Chinese handwriting recognition using hidden Markov models. In: Proceedings of the Internatinal Conference on Pattern Recognition, vol. 3, pp. 212–215. Québec (2002)Google Scholar
  46. 46.
    Feng, S., Manmatha, R., Mccallum, A.: Exploring the use of conditional random field models and HMMs for historical handwritten document recognition. In: 2nd Internatinal Conference on Document Image Analysis for Libraries (DIAL), pp. 8–37 (2006)Google Scholar
  47. 47.
    Fink G.A.: Markov Models for Pattern Recognition—From Theory to Applications. Springer, Heidelberg (2008)MATHGoogle Scholar
  48. 48.
    Fink, G.A., Plötz, T.: On appearance-based feature extraction methods for writer-independent handwritten text recognition. In: Proceedings of the Internatinal Conference on Document Analysis and Recognition, vol. 2, pp. 1070–1074. IEEE, Seoul, Korea (2005)Google Scholar
  49. 49.
    Fink, G.A., Plötz, T.: Unsupervised estimation of writing style models for improved unconstrained off-line handwriting recognition. In: Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition. IEEE, La Baule, France (2006)Google Scholar
  50. 50.
    Fink, G.A., Plötz, T.: Tutorial on Markov models for handwriting recognition. In: Proceedings of the Internatinal Conference on Document Analysis and Recognition. Curitiba, Brazil (2007)Google Scholar
  51. 51.
    Fink G.A., Plötz T.: Developing pattern recognition systems based on Markov models: the ESMERALDA framework. Pattern Recogn. Image Anal. 18(2), 207–215 (2008)CrossRefGoogle Scholar
  52. 52.
    Fiscus, J.: A post-processing system to yield reduced word error rates: recognizer output voting error reduction. In: Furui, S., Huang, B.H., Chu, W. (eds.) Proceedings of the Workshop on Automatic Speech Recognition and Understanding, pp. 347–352. Santa Barbara (1997)Google Scholar
  53. 53.
    Fujisawa H.: Robustness design of industrial strength recognition systems. In: Chaudhuri, B. (eds) Digital Document Processing: Major Diretions and Recent Advances, pp. 185–212. Springer, London (2007)CrossRefGoogle Scholar
  54. 54.
    Fujisawa H.: Forty years of research in character and document recognition—an industrial perspective. Pattern Recogn. 41, 2435–2446 (2008)CrossRefGoogle Scholar
  55. 55.
    Gader P.D., Keller J.M., Krishnapuram R., Chiang J.H., Mohamed M.A.: Neuronal and fuzzy methods in handwriting recognition. Computer 2, 79–86 (1997)CrossRefGoogle Scholar
  56. 56.
    Gauthier, N., Artières, T., Dorizzi, B., Ballinari, P.: Strategies for combining on-line and off-line information in an on-line handwriting recognition system. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 412–416. Seattle (2001)Google Scholar
  57. 57.
    Ge, Y., Huo, Q.: A study on the use of CDHMM for large vocabulary offline recognition of handwritten Chinese characters. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 334–338. Niagara on the Lake, Canada (2002)Google Scholar
  58. 58.
    Grandidier, F., Sabourin, R., Suen, C.Y.: Integration of contextual information in handwriting recognition systems. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 1252–1256. Edinburgh (2003)Google Scholar
  59. 59.
    Günter, S., Bunke, H.: A new combination scheme for HMM-based classifiers and its application to handwriting recognition. In: Proceedings of the International Conference on Pattern Recognition, vol. 2, pp. 332–337. Québec (2002)Google Scholar
  60. 60.
    Günter, S., Bunke, H.: Optimizing the number of states, training iterations and Gaussians in an HMM-based handwritten word recognizer. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 1, pp. 472–476. Edinburgh (2003)Google Scholar
  61. 61.
    Günter S., Bunke H.: HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components. Pattern Recogn. 37, 2069–2079 (2004)CrossRefGoogle Scholar
  62. 62.
    Huang X.D., Ariki Y., Jack M.A.: Hidden Markov Models for Speech Recognition. No. 7 in Information Technology Series. Edinburgh University Press, Edinburgh (1990)Google Scholar
  63. 63.
    Huang X.D., Jack M.A.: Semicontinuous hidden Markov models for speech signals. Comput. Speech Lang. 3(3), 239–251 (1989)CrossRefGoogle Scholar
  64. 64.
    Justino, E.J.R., El Yacoubi, A., Bortolozzi, F., Sabourin, R.: An off-line signature verification system using hidden Markov model and cross-validation. In: Proceedings of the XIII Brazilian Symposium on Computer Graphics and Image Processing, pp. 105–112 (2000)Google Scholar
  65. 65.
    Kaltenmeier, A., Caesar, T., Gloger, J.M., Mandler, E.: Sophisticated topology of hidden Markov models for cursive script recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 139–142 (1993)Google Scholar
  66. 66.
    Ko A.H.R., Sabourin R., de Souza Britto A. Jr.: Ensemble of HMM classifiers based on the clustering validity index for a handwritten numeral recognizer. Pattern Anal. Appl. J. 12(1), 21–35 (2009)CrossRefGoogle Scholar
  67. 67.
    Koerich, A.L., Britto, A.S., de Oliviera, L.E.S., Sabourin, R.: Fusing high- and low-level features for handwritten word recognition. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 151–156. La Baule, France (2006)Google Scholar
  68. 68.
    Koerich, A.L., Leydier, Y., Sabourin, R., Suen, C.Y.: A hybrid large vocabulary handwritten word recognition system using neuronal networks with hidden Markov models. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 99–104. Niagara on the Lake, Canada (2002)Google Scholar
  69. 69.
    Kundu A., He Y., Bahl P.: Recognition of handwritten words: first and second order hidden Markov model based approach. Pattern Recogn. 22(3), 283–297 (1989)CrossRefGoogle Scholar
  70. 70.
    Kundu, A., Hines, T., Phillips, J., Huyck, B.D., Van Guilder, L.C.: Arabic handwriting recognition using variable duration HMM. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 644–648. Curitiba, Brazil (2007)Google Scholar
  71. 71.
    Li Y., Zheng Y., Doermann D., Jaeger S.: Script-independent text line segmentation in freestyle handwritten documents. IEEE Trans. Pattern Anal. Mach. Intell. 30(8), 1313–1329 (2008)CrossRefGoogle Scholar
  72. 72.
    Likforman-Sulem L., Zahour A., Taconet B.: Text line segmentation of historical documents: a survey. Int. J. Doc. Anal. Recogn. 9(2), 123–138 (2007)CrossRefGoogle Scholar
  73. 73.
    Liwicki, M., Bunke, H.: Enhancing training data for handwritten recognition of whiteboard notes with samples from a different database. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 550–554. Seoul, Korea (2005)Google Scholar
  74. 74.
    Liwicki, M., Bunke, H.: Handwriting recognition of whiteboard notes. In: Proceedings of the 12th Conference of the International Graphonomics Society, pp. 118–122 (2005)Google Scholar
  75. 75.
    Liwicki, M., Bunke, H.: IAM-OnDB—an on-line English sentence database acquired from handwritten text on a whiteboard. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 956–961. Seoul, Korea (2005)Google Scholar
  76. 76.
    Lorigo L.M., Govindaraju V.: Offline Arabic handwriting recognition: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 712–724 (2006)CrossRefGoogle Scholar
  77. 77.
    Lowerre, B.T.: The HARPY speech recognition system. Ph.D. thesis, Carnegie-Mellon University, Department of Computer Science, Pittsburg (1976)Google Scholar
  78. 78.
    Lu, Z., Schwartz, R., Raphael, C.: Script-independent, HMM-based text line finding for OCR. In: Proceedings of the International Conference on Pattern Recognition, pp. 551–554. Barcelona, Spain (2000)Google Scholar
  79. 79.
    Madhvanath S., Kim G., Govindaraju V.: Chaincode contour processing for handwritten word recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 928–932 (1999)CrossRefGoogle Scholar
  80. 80.
    Mao, S., Rosenfeld, A., Kanungo, T.: Document structure analysis algorithms: a literature survey. In: Proceedings of the SPIE Electronic Imaging, pp. 197–207 (2003)Google Scholar
  81. 81.
    Märgner, V., El-Abed, H.: ICDAR 2005—Arabic handwriting recognition competition. In: Proceedings of the International Conference on Document Analysis and Recognition. Seoul, Korea (2005)Google Scholar
  82. 82.
    Märgner, V., El-Abed, H.: ICDAR 2007—Arabic handwriting recognition competition. In: Proceedings of the International Conference on Document Analysis and Recognition (2007)Google Scholar
  83. 83.
    Markov, A.A.: Open image in new window Open image in new window Open image in new window (Example of statistical investigations of the text of ,,Eugen Onegin”, wich demonstrates the connection of events in a chain). In: Open image in new window (Bulletin de l’Académie Impériale des Sciences de St.-Pétersbourg), pp. 153–162. Sankt-Petersburg (1913, in Russian)Google Scholar
  84. 84.
    Marti, U.V., Bunke, H.: Handwritten sentence recognition. In: Proceedings of the International Conference on Pattern Recognition, vol. 3, pp. 467–470. Barcelona (2000)Google Scholar
  85. 85.
    Marti, U.V., Bunke, H.: On the influence of vocabulary size and language models in unconstrained handwritten text recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 260–265. Seattle (2001)Google Scholar
  86. 86.
    Marti, U.V., Bunke, H.: Text line segmentation and word recognition in a system for general writer independent handwriting recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 159–163. Seattle (2001)Google Scholar
  87. 87.
    Marti U.V., Bunke H.: Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition systems. Int. J. Pattern Recogn. Artif. Intell. 15(1), 65–90 (2001)CrossRefGoogle Scholar
  88. 88.
    Marti U.V., Bunke H.: The IAM-database: an English sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recogn. 5(1), 39–46 (2002)MATHCrossRefGoogle Scholar
  89. 89.
    Menasri, F., Vincent, N., Augustin, E., Cheriet, M.: Shape-based alphabet for off-line Arabic handwriting recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 969–973. Curitiba, Brazil (2007)Google Scholar
  90. 90.
    Miletzki, U., Bayer, T., Schäfer, H.: Continuous learning systems: postal address readers with built-in learning capability. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 329–332. Bangalore, India (1999)Google Scholar
  91. 91.
    Morita, M., El Yacoubi, A., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Handwritten month word recognition on Brazilian bank cheques. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 972–976. Seattle (2001)Google Scholar
  92. 92.
    Morita, M., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Segmentation and recognition of handwritten dates. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 105–110. Niagara on the Lake, Canada (2002)Google Scholar
  93. 93.
    Natarajan P., Lu Z., Schwartz R., Bazzi I., Makhoul J.: Multilingual machine printed OCR. Int. J. Pattern Recog. Artif. Intell. 15(1), 43–63 (2001)CrossRefGoogle Scholar
  94. 94.
    Natarajan P., Saleem S., Prasad R., MacRostie E., Subramanian K.: Multi-lingual offline handwriting recognition using hidden Markov models: a script-independent approach. In: Doermann, D.S., Jaeger, S. (eds) Arabic and Chinese Handwriting Recognition: SACH 2006 Selected Papers, Lecture Notes in Computer Science, vol. 4768, pp. 231–250. Springer, Berlin (2008)Google Scholar
  95. 95.
    Nopsuwanchai R., Biem A., Clocksin W.F.: Maximization of mutual information for offline Thai handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1347–1351 (2006)CrossRefGoogle Scholar
  96. 96.
    Pechwitz, M., Maddouri, S.S., Märgner, V., Ellouze, N., Amiri, H.: IFN/ENIT-database of handwritten Arabic words. In: Proceedings of the 7th Colloque International Francophone sur l’Ecrit et le Document. Hammamet, Tunis (2002)Google Scholar
  97. 97.
    Pechwitz, M., Märgner, V.: HMM based approach for handwritten Arabic word recognition using the IFN/ENIT-database. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 890–894. Edinburgh (2003)Google Scholar
  98. 98.
    Pittman J.A.: Handwriting recognition: tablet PC text input. IEEE Comput. 40(9), 49–54 (2007)Google Scholar
  99. 99.
    Plamondon R., Srihari S.N.: On-line and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000)CrossRefGoogle Scholar
  100. 100.
    Plötz, T., Thurau, C., Fink, G.A.: Camera-based whiteboard reading: new approaches to a challenging task. In: Proceedings of the 11th International Conference on Frontiers in Handwriting Recognition, pp. 385–390. Montreal, Canada (2008)Google Scholar
  101. 101.
    Rigoll, G., Kosmala, A., Rottland, J., Neukirchen, C.: A comparison between continuous and discrete density hidden Markov models for cursive handwriting recognition. In: Proceedings of the International Conference on Pattern Recognition, vol. 2, pp. 205–209. Vienna (1996)Google Scholar
  102. 102.
    Schambach, M.P.: Determination of the number of writing variants with an HMM based cursive word recognition system. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 1, pp. 119–123. Edinburgh (2003)Google Scholar
  103. 103.
    Schambach, M.P.: Fast script word recognition with very large vocabulary. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 1, pp. 9–13. Seoul, Korea (2005)Google Scholar
  104. 104.
    Schambach, M.P., Rottland, J., Alary, T.: How to convert a Latin handwriting recognition system to Arabic. In: Proceedings of the International Conference on Document Analysis and Recognition (2008)Google Scholar
  105. 105.
    Schwartz, R., LaPre, C., Makhoul, J., Raphael, C., Zhao, Y.: Language-independent OCR using a continuous speech recognition system. In: Proceedings of the International Conference on Pattern Recognition, vol. 3, pp. 99–103. Vienna, Austria (1996)Google Scholar
  106. 106.
    Senior A.W., Robinson A.J.: An off-line cursive handwriting recognition system. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 309–321 (1998)CrossRefGoogle Scholar
  107. 107.
    Starner, T., Makhoul, J., Schwartz, R., Chou, G.: On-line cursive handwriting recognition using speech recognition methods. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. 125–128. Adelaide (1994)Google Scholar
  108. 108.
    Steinherz T., Rivlin E., Intrator N.: Offline cursive script word recognition—a survey. Int. J. Doc. Anal. Recogn. 2, 90–110 (1999)CrossRefGoogle Scholar
  109. 109.
    Su, T.H., Zhang, T.W., Huang, H.J., Zhou, Y.: HMM-based recognizer with segmentation-free strategy for unconstrained Chinese handwriting text. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 1, pp. 133–137. Curitiba, Brazil (2007)Google Scholar
  110. 110.
    Tay, Y.H., Pierre-Michel, L., Khalid, M., Knerr, S., Virad-Gaudin, C.: An analytical handwritten word recognition system with word-level discriminant training. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 726–730. Seattle (2001)Google Scholar
  111. 111.
    Toselli, A.H., Juan, A., Vidal, E.: Spontaneous handwriting recognition and classification. In: Proceedings of the International Conference on Pattern Recognition, vol. 1, pp. 433–436. Cambridge, UK (2004)Google Scholar
  112. 112.
    Touj, S.M., Ben Amara, N.E., Amiri, H.: A hybrid approach for off-line Arabic handwriting recognition based on a planar hidden Markov modeling. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 964–968. Curitiba, Brazil (2007)Google Scholar
  113. 113.
    Trier O.D., Taxt T.: Evaluation of binarization methods for document images. IEEE Trans. Pattern Anal. Mach. Intell. 17(3), 312–315 (1995)CrossRefGoogle Scholar
  114. 114.
    Vajda, S., Belaïd, A.: Structural information implant in a context based segmentation-free HMM handwritten word recognition system for Latin and Bangla scripts. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 1126–1130. Seoul, Korea (2005)Google Scholar
  115. 115.
    Vinciarelli A.: A survey on off-line cursive word recognition. Pattern Recogn. 35, 1433–1446 (2002)MATHCrossRefGoogle Scholar
  116. 116.
    Vinciarelli, A., Bengio, S.: Writer adaptation techniques in off-line cursive word recognition. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 287–291. Niagara on the Lake, Canada (2002)Google Scholar
  117. 117.
    Vinciarelli A., Bengio S., Bunke H.: Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 709–720 (2004)CrossRefGoogle Scholar
  118. 118.
    Vinciarelli, A., Luettin, J.: Off-line cursive script recognition based on continuous density HMM. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 493–498 (2000)Google Scholar
  119. 119.
    Vinciarelli A., Luettin J.: A new normalization technique for cursive handwritten words. Pattern Recogn. Lett. 22(9), 1043–1050 (2001)MATHCrossRefGoogle Scholar
  120. 120.
    Viterbi A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 13, 260–269 (1967)MATHCrossRefGoogle Scholar
  121. 121.
    Wang, W., Brakensiek, A., Kosmala, A., Rigoll, G.: Multi-branch and two-pass HMM modeling approaches for off-line cursive handwriting recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 231–235. Seattle (2001)Google Scholar
  122. 122.
    Wang, W., Brakensiek, A., Rigoll, G.: Combining HMM-based two-pass classifiers for off-line word recognition. In: Proceedings of the International Conference on Pattern Recognition, vol. 3, pp. 151–154. Québec (2002)Google Scholar
  123. 123.
    Wienecke, M., Fink, G.A., Sagerer, G.: Experiments in unconstrained offline handwritten text recognition. In: Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition. IEEE, Ontario, Canada (2002)Google Scholar
  124. 124.
    Wienecke, M., Fink, G.A., Sagerer, G.: Toward automatic video-based whiteboard reading. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 87–91. IEEE, Edinburgh, Scotland (2003)Google Scholar
  125. 125.
    Wienecke M., Fink G.A., Sagerer G.: Toward automatic video-based whiteboard reading. Int. J. Doc. Anal. Recogn. 7(2–3), 188–200 (2005)CrossRefGoogle Scholar
  126. 126.
    Xu, Q., Kim, J.H., Lam, L., Suen, C.Y.: Recognition of handwritten month words on bank cheques. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 111–116. Niagara on the Lake, Canada (2002)Google Scholar
  127. 127.
    Xue H., Govindaraju V.: Hidden Markov models combining discrete symbols and continuous attributes in handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(3), 458–462 (2006)CrossRefGoogle Scholar
  128. 128.
    Young S.: A review of large-vocabulary continuous-speech recognition. IEEE Signal Process. Mag. 13(9), 45–57 (1996)CrossRefGoogle Scholar
  129. 129.
    Zimmermann, M., Bunke, H.: Automatic segmentation of the IAM off-line database for handwritten English text. In: Proceedings of the International Conference on Pattern Recognition, vol. 4, pp. 35–39. Québec (2002)Google Scholar
  130. 130.
    Zimmermann, M., Bunke, H.: Hidden Markov model length optimization for handwriting recognition systems. In: Proceedings of the International Workshop on Frontiers in Handwriting Recognition, pp. 369–374. Niagara on the Lake, Canada (2002)Google Scholar

Copyright information

© The Author(s) 2009

Authors and Affiliations

  1. 1.Intelligent Systems Group, Robotics Research InstituteTechnische Universität DortmundDortmundGermany

Personalised recommendations