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    Statistical Language Models for Graphical Object Recognition

    Keyes, Laura and O'Sullivan, Andrew and Winstanley, Adam C. (2004) Statistical Language Models for Graphical Object Recognition. ITB Journal, 10. pp. 25-36.

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    This paper explores automatic recognition and semantic capture in vector graphics for graphical information systems. The low-level graphical content of graphical documents, such as a map or architectural drawing, are often captured manually and the encoding of the semantic content seen as an extension of this. The large quantity of new and archived graphical data available on paper makes automatic structuring of such graphical data desirable. A successful method for recognising text data uses statistical language models. This work will investigate and evaluate similar and adapted statistical models (Statistical Graphical Langauge Models, SGLM) to graphical languages based on the associations between different classes of object in a drawing to automate the structuring and recognition of graphical data.

    Item Type: Article
    Keywords: Statistical Language Models; Semantic Modelling; CAD Drawings; Graphical Object Recognition; Statistical Graphical Langauge Models; Operation and Maintenance; Information System;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 10383
    Depositing User: Dr. Adam Winstanley
    Date Deposited: 07 Jan 2019 15:30
    Journal or Publication Title: ITB Journal
    Publisher: Institute of Technology Blanchardstown
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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