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Handbook of Document Image Processing and Recognition pp 391–425Cite as

Continuous Handwritten Script Recognition

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Abstract

The transcription of written text images is one of the most challenging tasks in document analysis since it has to cope with the variability and ambiguity encountered in handwritten data. Only in a very restricted setting, as encountered in postal addresses or bank checks, transcription works well enough for commercial applications. In the case of unconstrained modern handwritten text, recent advances have pushed the field towards becoming interesting for practical applications. For historic data, however, recognition accuracies are still far too low for automatic systems. Instead, recent efforts aim at interactive solutions in which the computer merely assists an expert creating a transcription. In this chapter, an overview of the field is given and the steps along the processing chain from the text line image to the final output are explained, starting with image normalization and feature representation. Two recognition approaches, based on hidden Markov models and neural networks, are introduced in more detail. Finally, databases and software toolkits are presented, and hints to further material are provided.

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Further Reading

  • Fischer A, Keller A, Frinken V, Bunke H (2011, submitted) Lexicon-free handwritten word spotting using character HMMs

    Google Scholar 

  • Frinken V, Fischer A, Manmatha R, Bunke H (2012) A novel word spotting method based on recurrent neural networks. IEEE Trans Pattern Anal Mach Intell. Accepted for publication

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  • Goodman JT (2001) A bit of progress in language modeling – extended version. Technical report MSR-TR-2001-72, Microsoft Research, One Microsoft Way Redmond, WA 98052, 8

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  • Graves A (2012) Supervised sequence labelling with recurrent neural networks. Springer, Heidelberg/New York/Dordrecht/London

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  • Liwicki M, Graves A, Bunke H (2012) Neural networks for handwriting recognition. In: Ogiela MR, Jain LC (eds) Computational intelligence paradigms in advanced pattern classification, vol 386/2012. Springer, Berlin/Heidelberg, pp 5–24

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  • Plötz T, Fink G (2011) Markov models handwriting recognition. Springer, London/Dordrecht/ Heidelberg/New York

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  • Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

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  • Toselli AH, Vidal E, Casacuberta F (2011) Multimodal interactive pattern recognition and applications. Springer, London/New York

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Vision Center, Autonomous University of Barcelona, Bellaterra, Spain

    Volkmar Frinken

  2. Research Group on Computer Vision and Artificial Intelligence (FKI), Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland

    Horst Bunke

Authors
  1. Volkmar Frinken
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  2. Horst Bunke
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Editor information

Editors and Affiliations

  1. University of Maryland, College Park, MD, USA

    David Doermann

  2. Université de Lorraine, Nancy, France

    Karl Tombre

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Frinken, V., Bunke, H. (2014). Continuous Handwritten Script Recognition. In: Doermann, D., Tombre, K. (eds) Handbook of Document Image Processing and Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-859-1_12

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