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General Architecture

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Markov Models for Handwriting Recognition

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

As a first step of document understanding a digital image of the document to be analyzed or the trajectory of the pen used for writing needs to be captured. From this raw data the relevant document elements (e.g., text lines) need to be segmented. These are then subject to a number of pre-processing steps that aim at reducing the variability in the appearance of the writing by applying a sequence of normalization operations. In order to be processed by a handwriting recognition system based on Markov models, text-line images and raw pen trajectories are then converted into a sequential representation—which is quite straight-forward for online data but requires some “trick” in the offline case. Based on the serialized data representation features are computed that characterize the local appearance of the script. These are fed into a Markov-model based decoder that produces a hypothesis for the segmentation and classification of the analyzed portion of handwritten text—usually as a sequence of word or character hypotheses.

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Notes

  1. 1.

    Especially when processing machine printed documents where it is usually clear that the document image only shows the document to be analyzed, this initial segmentation of relevant document structures is referred to as layout analysis (cf. e.g., [1]).

  2. 2.

    As any document analysis system needs to extract relevant textual items, e.g., words or lines, from the document image or the raw pen trajectory prior to recognition, several preprocessing steps are necessary. These perform tasks which can also be termed “segmentation”. Though there are first approaches to perform, e.g., line separations using HMMs [3], in this respect traditional and MM-based systems are still quite similar. Therefore, in this book we focus on the segmentation at the level of character or word sequences where MM-based approaches can show their strengths.

  3. 3.

    The very basic splitting of the input data at this early stage is fundamentally different from the much more complex segmentation of handwriting data into meaningful parts as, e.g., characters or words. See also Sect. 4.1 for a discussion.

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Authors and Affiliations

  1. Culture Lab, School of Computing Science, Newcastle University, Grand Assembly Rooms, King’s Walk, Newcastle upon Tyne, NE1 7RU, UK

    Thomas Plötz

  2. Department of Computer Science, Technische Universität Dortmund, Otto-Hahn-Strasse 16, 44227, Dortmund, Germany

    Gernot A. Fink

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  1. Thomas Plötz
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  2. Gernot A. Fink
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Correspondence to Thomas Plötz .

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© 2011 Thomas Plötz

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Plötz, T., Fink, G.A. (2011). General Architecture. In: Markov Models for Handwriting Recognition. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-2188-6_2

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  • DOI: https://doi.org/10.1007/978-1-4471-2188-6_2

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2187-9

  • Online ISBN: 978-1-4471-2188-6

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