Summary
This paper talks about a pictorial approach for recognizing strokes of a certain classes. The approach is describing the parts of the big picture in relation to other parts in the scene. The lower parts are recognized by a very simple feature set such as bounding boxes, slopes, diagonals etc.
Each model is trained with labeled data and a provability distribution is computed for each object. Then the recognition process turns out to be running an ML search for the given case. The model was tested on facial recognition.
Discussion
The paper reminded me LADDER where the constrains were declared between lower level shapes to recognize higher level shapes. For sure, this is a common recognition approach, but it can fail drastically if recognition of lower level shapes fails.
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