Recognizing Hand-Drawn Electrical Circuit Symbols with Attributed Graph Matching

We propose a model-based approach to the recognition of hand-drawn electrical circuit symbols under arbitrary unknown similarity transformations (translation, rotation, and scale). A hybrid representation, called an attributed graph (AG), incorporating structural and statistical characteristics of image patterns, is used for matching an unknown symbol to models. We describe experimental results that suggest that AG matching is effective and efficient for this problem.

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