Stroke extraction is one of the most important tasks in areas of computer graphics and document analysis. So far, data-driven methods are believed to perform relatively well, which use the pre-processed characters as templates. However, how to accurately extract strokes of characters is still a tough and challenging task because there are various styles of characters, which may vary a lot from the template character. To solve this problem, we build a font skeleton manifold in which we can

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... find a most similar character as a template by traversing the locations in the manifold. Because of the similar structure and font style, the point set registration of the template character with the target character would be much more effective and accurate. Experimental results on characters in both printing style and handwriting style reveal that our method using manifold learning has a better performance in the application of stroke extraction for Chinese characters.