On-Line Character Recognition Adaptively Controlled by Handwriting Quality

On-line character recognition which can adapt to handwriting quality is proposed. In character recognition, it is di cult to recognize both clearly and roughly written characters accurately. For Japanese characters, the number of strokes is often slightly varied when characters are written roughly. In a previous method, the ranges of the number of strokes were set widely enough for recognition; however, these ranges were not optimal for clearly written characters. The proposed method controls a distribution model of the number of strokes adaptively according to handwriting quality, and it uses this model for pre-candidate selection and ne classi cation. Recognition experiments demonstrated that the proposed method has greater recognition accuracy and speed than the previous method. In particular, accuracy was improved from 91.4% to 94.3% and speed was increased by about 50% when recognizing clearly written data.