COMMENTS ON OTHERS:
The paper proposes to use formal statistical analysis methods to identify key ink features to improve recognition. The features measure aspects of an ink stroke’s curvature, size, time, intersections and use similar aspects to detect relationships between strokes. The proposed approach begins with investigating a range of possible ink features, how to collect these feature data and analysis, and initial result of an evaluation of a text/shape divider based on these key ink feature set. The proposed feature set includes 46 features and grouped into 7 categories of size, time, intersection, curvature, pressure, operating system recognition values and inter-stroke gaps.
Am I missing it or the 46 feature set is not actually listed on the paper or what? Also I’m wondering this particular technique is just to divide the whole sketch system into 2 groups, text or shape, and not to identify further each individual component against a library component? The authors state that they concentrated on identifying the distinguishing features of text versus shape strokes using a formal method for optimal ink feature selection; this means only identifying those stated 2 groups? I kow this is something important when we have a system like COA where possibly a text and shapes mixed with each other, but how feasible approach this is when recognizing something within a time limit say like sixty seconds? We have a recognition engine to separate text and shape and then again apply a recognition engine for each shape and text separately?