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Handwritten Address Recognition Using Hidden Markov Models

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Reading and Learning

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2956))

Abstract

In this paper several aspects of a recognition system for cursive handwritten German address words (cities and streets) are described. The recognition system is based on Hidden Markov Models (HMMs), whereat the focus is on two main problems: the changes in the writing style depending on time or regional differences and the difficulty to select the correct (complete) dictionary for address reading. The first problem leads to the examination of three different adaptation techniques: Maximum Likelihood (ML), Maximum Likelihood Linear Regression (MLLR) and Scaled Likelihood Linear Regression (SLLR). To handle the second problem language models based on backoff character n-grams are examined to evaluate the performance of an open vocabulary recognition (without dictionary). For both problems the determination of confidence measures (based on the frame-normalized likelihood, a garbage model, a two-best recognition or an unconstrained character decoding) is important, either for an unsupervised adaptation or the detection of out of vocabulary words (OOV). The databases, which are used for recognition, are provided by Siemens Dematic (SD) within the Adaptive READ project.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, University Duisburg, 47057, Duisburg, Germany

    Anja Brakensiek & Gerhard Rigoll

  2. Inst. for Human-Machine Communication, Munich University of Technology, 80290, Munich, Germany

    Gerhard Rigoll

Authors
  1. Anja Brakensiek
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  2. Gerhard Rigoll
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Editors and Affiliations

  1. Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany

    Andreas Dengel

  2. German Research Center for Artificial Intelligence (DFKI), P.O.Box 2080, 67608, Kaiserslautern, Germany

    Markus Junker

  3. Institute for Human Factors and Technology Management, University of Stuttgart, Nobelstraße 12, 70569, Stuttgart, Germany

    Anette Weisbecker

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© 2004 Springer-Verlag Berlin Heidelberg

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Brakensiek, A., Rigoll, G. (2004). Handwritten Address Recognition Using Hidden Markov Models. In: Dengel, A., Junker, M., Weisbecker, A. (eds) Reading and Learning. Lecture Notes in Computer Science, vol 2956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24642-8_7

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  • DOI: https://doi.org/10.1007/978-3-540-24642-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21904-0

  • Online ISBN: 978-3-540-24642-8

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