COMMENTS:
SUMMARY:
The text vs graphics recognition method presented in this paper provides a possible classification to a grouping of strokes. The system uses a neighborhood graph to show strokes that are in close proximity to each other and groups strokes whose vertices are connected in the neighborhood graph. The authors use search-based optimization to determine the best grouping and labeling match. The authors achieved a 97% percent accuracy rate for the for both grouping and recognition.
DISCUSSION:
The method in this paper has a similar goal as the Bishop paper. However, instead of using the features of the strokes and spaces between them, this approach uses a neighborhood graph to determine the stroke groupings. I thought it was interesting that the recognizer's thresholds are learned from a set of examples instead of from a list of predefined features. This prevents the recognizer from the being constrained to recognize a specific set of symbols.
Did anyone else find a weird sentence in the 4th paragraph of the Previous Work section?
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