Robust on-line handwritten object retrieval using a similarity measure based on overlap area of circles

To retrieve an arbitrary part of handwritten objects from a database by a handwritten query and be able to deal with subtle variations of object position and shape, the following retrieval method is proposed: (1) Strokes represented by chain codes are classified using a clustering technique. (2) A target object is retrieved using a similarity measure based on an overlap area of circles whose positions and sizes are determined by positions and category types of strokes. We examine the effectiveness of the proposed method using a database with 707 strokes and 30 roughly handwritten queries. As a result, top-10 retrieval accuracy of 97% was obtained.