An extended system for labeling graphical documents using statistical language models

Authors:
Andrew O’Sullivan;Laura Keyes;Adam Winstanley
Affiliations:
School of Informatics and Engineering, Institute of Technology Blanchardstown, Dublin 15, Ireland;School of Informatics and Engineering, Institute of Technology Blanchardstown, Dublin 15, Ireland;Department of Computer Science, NUI Maynooth, Maynooth, Co. Kildare, Ireland
Venue:
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Year:
2005

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Abstract

This paper describes a proposed extended system for the recognition and labeling of graphical objects within architectural and engineering documents that integrates Statistical Language Models (SLMs) with shape classifiers. Traditionally used for Natural Language Processing, SLMS have been successful in such fields as Speech Recognition and Information Retrieval. There exist similarities between natural language and technical graphical data that suggest that adapting SLMs for use with graphical data is a worthwhile approach. Statistical Graphical Language Models (SGLMs) are applied to graphical documents based on associations between different classes of shape in a drawing to automate the structuring and labeling of graphical data. The SGLMs are designed to be combined with other classifiers to improve their recognition performance. SGLMs perform best when the graphical domain being examined has an underlying semantic system, that is; graphical objects have not been placed randomly within the data. A system which combines a Shape Classifier with SGLMS is described.