COMMENTS ON OTHERS:
The paper proposes a spatial framework for simultaneous grouping and recognition of shapes and symbols in free-form ink diagrams. The recognition is done by linking each stroke into a proximity graph and then using a discriminative classifier to classify connected subgraphs as either known symbol or invalid combination of strokes. In the preprocessing the graph is created with the nodes corresponds to strokes and edges to the strokes in close proximity. Then a dynamic programming approach was used to iterate on nodes and discriminated recognition was applied on the set. The classifier is named as AdaBoost and features are the Viola-Jones image filters to evaluate each stroke group. Authors also note that it is possible to input strokes which may not make up a shape at all called garbage shapes.
The paper is extremely short and not enough information provided with the recognizer, and in this case the most important part of this work. Much of the discussions in first few chapters are about the previous work, the preprocessing and the search tree on the neighborhood graph making process. The authors mentioned a use of dynamic programming in the content somewhere but not sure A* is the best method to get the optimum results. In my opinion the sketches tend to represent more of a close loop connection of strokes from start to end and this makes it easy to find an optimum path using a forward backward algorithm or using Viterbi.