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A Language Model for Improving the Graph-Based Transcription Approach for Historical Documents

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Advances in Artificial Intelligence -- IBERAMIA 2014 (IBERAMIA 2014)

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Abstract

Language Models (LMs) capture the contextual dependencies of a language and assign higher probabilities to well-formed sequences of words. For that reason, LMs have been commonly used in generic handwriting recognition, improving recognition results. In this paper, we present the integration of a Language Model along with a dictionary into a graph-based recognizer, which aims at transcribing handwritten historical documents. The results of such integration show a significant improvement on word accuracy when applied to our corpora.

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Author information

Authors and Affiliations

  1. Universidad La Salle, Arequipa, Perú

    Graciela Lecireth Meza-Lovón

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  1. Graciela Lecireth Meza-Lovón
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Correspondence to Graciela Lecireth Meza-Lovón .

Editor information

Editors and Affiliations

  1. Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Ana L.C. Bazzan

  2. Pontifica Universidad Católica (PUC), Santiago de Chile, Chile

    Karim Pichara

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Meza-Lovón, G.L. (2014). A Language Model for Improving the Graph-Based Transcription Approach for Historical Documents. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_19

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  • DOI: https://doi.org/10.1007/978-3-319-12027-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12026-3

  • Online ISBN: 978-3-319-12027-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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