Studies of Radical Model for Retrieval of Cursive Chinese Handwritten Annotations

Our research focuses on Chinese online ink matching that tries to match handwritten annotations with handwritten queries without attempting to recognize them. Previously, we proposed a semantic matching scheme that uses elastic matching with a dynamic programming approach based on the radical model of Chinese characters. By means of semantic matching, a handwritten annotation may also be retrieved independently of writers via typed text query, or stored texts can be retrieved by handwritten queries. This work concerns with the behavior of the previously proposed radical model in several aspects including character normalization, stroke segmentation, structural information, dynamic programming costs and schemes. Based on our study, a new radical model is proposed. As a result, the recall of retrieval by handwritten query reaches 90% for the first hit (an improvement of 20% over previous results) and the recall by text query reaches 80% when top 20 matches are returned.