Sketched symbol recognition using Latent-Dynamic Conditional Random Fields and distance-based clustering |
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Authors: | Vincenzo Deufemia Michele Risi Genoveffa Tortora |
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Affiliation: | Department of Management and Information Technology, University of Salerno, 84084 Fisciano (SA), Italy |
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Abstract: | In this paper we propose a two-stage method for recognizing sketched symbols that combine the use of a discriminative model, for labeling symbol strokes and a distance-based clustering algorithm, for grouping the labels belonging to the same symbol. In the first stage, we employ Latent-Dynamic Conditional Random Field (LDCRF), a discriminative model able to analyze the features of unsegmented sequences of strokes by taking into account spatio-temporal information, and to classify the symbol parts by considering contextual information. In the second stage, the labels obtained from LDCRF are grouped into symbol labels by using a distance-based clustering algorithm which takes into account the geometric relationships among strokes. The effectiveness of our method has been evaluated on the domain of electric circuit diagrams achieving accuracy values varying between 81.3% and 91.0%. |
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Keywords: | Symbol recognition Diagram understanding Sketched diagrams Discriminative models Stroke clustering Electric circuit diagrams |
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