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Graphical object recognition using statistical language models
Keyes, Laura; O'Sullivan, Créidhe; Winstanley, Adam C.
This paper describes a proposed system for the recognition and labeling of graphical objects within architectural and engineering documents that integrates statistical language models (SLMs) with traditional classifiers. SLMs are techniques used with success in natural language processing (NLP) for use in such tasks as speech recognition and information retrieval. This research proposes the adaptation of SLMs for use with graphical notation i.e. statistical graphical language model (SGLMs). Reasoning of the similarities between natural language and technical graphics is presented and the proposed use of SGLM for graphical object recognition is described.
Keyword(s): statistical graphical language model; graphical object recognition; statistical language models; architectural documents; engineering documents; natural language processing; speech recognition; information retrieval
Publication Date:
2005
Type: Book chapter
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): Keyes, Laura and O'Sullivan, Créidhe and Winstanley, Adam C. (2005) Graphical object recognition using statistical language models. In: Eighth International Conference on Document Analysis and Recognition, 2005. Proceedings. IEEE, pp. 1095-1099. ISBN 0769524206
Publisher(s): IEEE
File Format(s): other
Related Link(s): http://eprints.maynoothuniversity.ie/8104/1/AW-Graphical-2005.pdf
First Indexed: 2017-04-01 05:53:47 Last Updated: 2018-09-15 06:06:39