Diagram structure recognition by Bayesian conditional random fields

Hand-drawn diagrams present a complex recognition problem. Elements of the diagram are often individually ambiguous, and require context to be interpreted. We present a recognition method based on Bayesian conditional random fields (BCRFs) that jointly analyzes all drawing elements in order to incorporate contextual cues. The classification of each object affects the classification of its neighbors. BCRFs allow flexible and correlated features, and take both spatial and temporal information into account. BCRFs estimate the posterior distribution of parameters during training, and average predictions over the posterior for testing. As a result of model averaging, BCRFs avoid the overfitting problems associated with maximum likelihood training. We also incorporate automatic relevance determination (ARD), a Bayesian feature selection technique, into BCRFs. The result is significantly lower error rates compared to ML- and MAP-trained CRFs.