Graphical object recognition using statistical language models

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.