| Summary: | 碩士 === 大同大學 === 資訊工程學系(所) === 98 === In recent years, traditional text input devices are changed gradually to handwriting. Though handwriting board and touchpad are easy to use, they are inconvenient to carry. This paper proposes a video handwritten Chinese character recognition system using stroke segmentation and on-line model. Firstly, the location of fingertip is extracted and the fingertip trajectory is recorded for recognition. The trajectory is straight line approximated by finding the turning points of strokes. Owing to the loss of depth information, it is unknown the user is going to write or move the fingertip. Therefore ,character writing habits are utilized to develop rules for pen-up and pen-down strokes classification. The main idea is to find impossible strokes which represent pen-up strokes for moving fingertip. The first case of pen-up stroke is from right to left or bottom to up. The second case is that the pen-up stroke would not exist between two parallel strokes. The third case is that the next pen-down stroke would not be of the opposite direction with the current one. The forth case is pen-up stroke between left and right character components. And the last case is pen-up stroke between upper on lower character component. These rules are used to segment the character into pen-down and pen-up strokes. In addition to stroke direction, stroke types, stroke length ratio, and angle between two consecutive strokes are also used to build the character online model as a four tuple continuous sequence of string. For characters written with multiple ways, we could build more than one online model for these characters. Minimum edit distance by dynamic programming is deployed to match the input character on-line string with stored online character models for recognition. In experiments, we build about 1000 Chinese character on-line models. The recognition system is tested with five persons writing each of the 1000 Chinese characters three times. There are totally 548726 images with camera of capturing speed 30 frames per second. The accuracy of fingertip tracking is 98.88% with processing speed 12.6 times per second. The accuracy of pen-up and pen-down stroke segmentation is 91.56% and the accuracy of character recognition is 93.61%. These results demonstrate that the proposed method could be used to input characters by fingertip efficiently.
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