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Handwriting recognition and user interface

Research members: Dr. Masaki Nakagawa, Dr. Bilan Zhu

Research fields: Frontiers of informatics, Science education/Educational technology, Cultural assets study and museology

Departments: Institute of Engineering

Keywords: pattern recognition, human interface, information educational engineering, archaeological information

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Our research is being focused on "enhancing handwriting interfaces" which is aimed to provide creative human computer interfaces by handwriting.

Although character recognition is language dependent, handwriting itself is borderless. English, Chinese, Arabic, Japanese or even pictorial languages can be expressed with a single pen. With handwriting, one can express one's thinking most easily using pen-trace patterns. Moreover, thinking is not interrupted by the actions for handwriting. Thinking and handwriting forms a positive feedback to grow and clarify one's idea. These natures of handwriting are suited for creative work rather than labor intensive tasks.

To realize creative human computer interfaces by handwriting while inheriting the above natures, handwriting recognition technology, handwriting human interfaces as well as applications must be studied and enhanced.

Even without machine recognition, however, there are many applications where the pen is mightier than the mouse. With recognition, the power of handwriting is greatly extended.

Reference articles and patents

Bilan Zhu and M. Nakagawa: Building a Compact Online MRF Recognizer for Large Character Set by Structured Dictionary Representation and Vector Quantization Technique, Pattern Recognition, Vol. 47, No. 3, pp. 982-993 (2014.3).

Bilan Zhu and M. Nakagawa: A Robust Method for Coarse Classifier Construction from a Large Number of Basic Recognizers for On-line Handwritten Japanese Character Recognition, Pattern Recognition, Vol. 47, No. 2, pp. 685-693 (2014.2).

X-D. Zhou, D-H. Wang, F. Tian, C-L. Liu, and M. Nakagawa: Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 35, No. 10, pp. 2413-2426 (2013.10).

M. Nakagawa, Bilan Zhu, and Motoki Onuma: A Model of On-line Handwritten Japanese Text Recognition Free from Line Direction and Writing Format Constraints, IEICE Trans. Inf


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