As-compact-as-possible vectorization for character images

It is quite time-consuming to produce high-quality compact font libraries, especially for north-east Asia language systems that contain thousands of characters. However, existing vectorization algorithms either generate results with large storage requirements, or lose most stylish details after vectorization. To solve this problem, we propose a novel data-driven vectorization algorithm for character images to make the number of control points on vectorized contours as few as possible while preserving significant details. Experimental results demonstrate that our method clearly outperforms other state-of-the-art approaches by not only preserving most stylish features but also dramatically reducing the size of vectorized fonts.