How to match a drawn Chinese character to a database of stroke animations?

How to match a drawn Chinese character to a database of stroke animations

• I have a database of Chinese character stroke animations. Each entry is basically a set of points which shows how and in which order a character should be drawn. To make lookup easier, I would like to allow the user to manually draw a character with the mouse, then my software would propose some possible matches (like it's done on the iPhone). I searched on Google but didn't find anything relevant. I'd like to know what algorithms or research paper I need to look at. I guess it has something to do with shape recognition, except that the order in which the shape (Chinese character) is drawn is also relevant. Any suggestion?

• Answer:

  You were looking for scholarly articles, I am surprised you did not find anything. Here's what a quick search through Google Scholar with keywords path stroke recognition and handwriting stroke recognition got me: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=824821: Looks like a good place to start. An explanation of digital handwriting representation, and handwriting recognition techniques (with and without the trajectory data of the stroke) Does not deal specifically with Chinese characters, but I think it might be applicable. http://www.sciencedirect.com/science/article/pii/0031320382900322: It looks quite old, but is dealing with stroke segmentation, or to quote "a way of breaking down a character to a set of consecutive partial strokes". It is the reverse of what you want (you said you already have info about strokes), but maybe understanding the stroke representation might help you get on the right way. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=479986&tag=1: Again, not Chinese characters, but mathematical (which is still more "exotic" than regular text). Nice because it proposed a technique based on probabilities. http://www.sciencedirect.com/science/article/pii/S0031320300001655: I think the title says it all. Looks like exactly what you want, and promises promising results. One more thing to add: The most recent reference in the above bunch is as old as 2000. That indicates that what you want to do is probably a well-researched area, with tried out state-of-the-art techniques. It should not have been hard to look for reference material. Just to clarify, I did not actually read any of these articles. I just did my usual preliminary research step: go to article databases (google scholar, mendeley, ieeexplore...), type in a few keywords. Open the ones with promising titles, close one with completely uninteresting abstracts. Read the remaining abstracts more carefully, and select the ones that seem relevant. Having said this, not all the articles I proposed might be relevant, but judging from experience, they most probably are. I hope this helps. Also, if you have done any research in the meantime, please edit it in your question and share with the community. It looks like an interesting topic.