Prof. Xiwen Zhang
Xiwen Zhang is currently a Professor of Digital Media Department, School of Information Science in the Beijing Language and Culture University. He worked as an associated professor from 2002 to 2007 at the Human-computer interaction Laboratory, Institute of Software, Chinese Academy of Sciences. From 2005 to 2006 he was a Post doctor advised by Prof. Michael R. Lyu in the Department of Computer Science and Engineering, the Chinese University of Hong Kong. From February to April in 2001 he was a Research Assistant by Dr. KeZhang Chen in the Department of Mechanical Engineering, the University of Hong Kong. From 2000 to 2002 he was a Post doctor advised by Prof. ShiJie Cai in the Computer Science and Technology department, Nanjing University. Prof. Zhang's research interests include pattern recognition, computer vision, and human-computer interaction, as well as their applications in digital image, digital video, and digital ink. Prof. Zhang has published over 60 refereed journal and conference paper.
Speech Title: "Intelligently Extracting Information from Digital Ink Chinese Text by Junior International Students"
Abstract: Chinese characters have complex structures. Their writing plays an import role in learning Chinese. Junior international students can use digital pen to record their handwriting as digital ink. Various information can be extracted from the digital ink text, such as text line, Chinese characters, stroke errors, shape normalization. Digital ink is a new media compared with digital image and digital video. It is captured from handwriting and freehand drawing using digital pen. Point samples are captured by digital pens, containing positions, time stamp, and pressures. A stroke is a list of sampling points from pen down and movement to pen up. A list of strokes consists of a digital ink. Digital ink Chinese text are stroke sets, have neither text line, nor Chinese characters. Digital ink Chinese texts written by junior international students contain many information including errors and unnormal issues. It is difficult to recognize them. We proposed some intelligent methods to extract information, such as adaptive segmentation based on statistics analysis, classification using machine learning, stroke matching using Genetic Algorithm, evaluating the normalization for entire characters and their components using knowledge bases. With developing new intelligent methods and collecting more data, more valued information can be extracted.