目的 瓦当是珍贵的历史文化遗产。为了进行瓦当的数字化保护和瓦当文字的自动识别，针对瓦当图像高磨损、高噪声和拓扑复杂的特点，提出基于梯度矢量流场改进的level set骨架提取算法。方法 算法在传统level set骨架算法的基础上对中间函数进行改进，引入基于修正梯度矢量流场的中间函数替代传统的基于欧氏距离场的中间函数，主要通过两次速度不同的波传播实现，因此提高了算法的自动性和精确性。结果 面对构建的标准模型，算法所提骨架线与标准骨架线的平均匹配度为98.03%，骨架均为单像素宽，居中性良好。面对各种噪声，本文算法所提骨架线与不加噪声骨架线的平均匹配度为99.15%，算法的抗噪性强。面对拓扑复杂模型，算法得到的骨架与原图像拓扑一致性、连通性、光滑性良好。结论 实验结果表明，本文算法提取的骨架性能良好，算法抗噪性强，对拓扑复杂物体亦有较好结果，是一种有效的骨架提取算法。

Objective Eaves are precious Chinese heritage which possess profound historical and cultural significance. However, eaves' characters images have obvious characteristics of high wear resistance, high noise and complex topology. In order to achieve eaves' characters recognition and make a contribution to digital protection of cultural heritage objects, a new Method based on improved gradient vector flow field is proposed to extract skeleton of eaves' characters.Method The algorithm proposed in this paper is based on classic level set Methods which are usually implemented by fast matching Method. What is different is that traditional medial function of level set Method is replaced by gradient vector flow based medial function, which is more automatic and accurate. It is also the innovation point of this paper. The new algorithm is mainly achieved by two wave propagations.Result Experiments verify the effectiveness and accuracy of the algorithm. The skeleton is 98.03% similar to the standard skeleton of specific models constructed by Matlab2012a. when Gaussian, multiplicative, salt and pepper noise are added into the image, we got a skeleton 99.15% similar to the former one without any noise in it. Improved level set Method surpasses Hilditch thinning algorithm and distance transform algorithm and gets the best skeleton of eaves' characters, which is homotopy, thinness, centered and smoothness.Conclusion The Results of the experiments indicate that our algorithm proves to be a useful and effective technique to extract skeleton of 2D objects with complex topology.