From humans to handwriting to computer and back

Summary form only given. There has been much research on human thought processes, but little on the inference of such knowledge into the computer, for the purpose of handwriting recognition. Although modern recognition engines can recognize many handwritten symbols and cursive scripts with a high level of accuracy, they often make foolish or unreasonable mistakes. These engines often act like black boxes, which is why they make such mistakes on characters that would normally be easily recognized by human beings. To break through this level of accuracy, we have to look back and explore more human aspects, to better understand their thought processes and to discover the ways and means humans acquire recognition knowledge. After that, we can infer this knowledge to the computer to create more intelligent computational recognizers. This talk aims to share our findings with you related to the recognition of handwritten characters. It summarizes the results of several experiments we conducted in the past while attempting to understand the way humans write and recognize handwritten characters. Our investigations include handwriting education in elementary schools, handwriting models, stroke sequences, the legibility of different character shapes, left-handedness and right-handedness, the creation of databases for learning and testing, the derivation of the boundary between similar samples, and the pitfalls of current recognition algorithms and remedies. This talk concludes with highlights on the results of these studies and their applications to improve the reliability of computer recognition of handwritten characters.