Visual gesture recognition enables natural human-machine interaction, and writing characters in gesture can convey rich information of intention. However, the recognition of character strings in gesture is challenging because multiple characters are in a single-stroke trajectory without pen lift information. We propose a lexicon-driven approach for gesture character string recognition. Using a lexicon of words to guide character segmentation and recognition, and meanwhile combining the geometric scores of characters and redundant segments with character classification score, we can achieve fairly high recognition accuracy on one-stroke character strings. For experiments, we collected 1,590 gesture strings in 100 word classes of television channel names, and achieved string-level recognition accuracy over 80% on the test set.
['Fei Yin', 'Pai pai Liu', 'Lin Lin Huang', 'Cheng-Lin Liu']
Lexicon-driven recognition of one-stroke character strings in visual gesture