Functional Components of Variation in Handwriting
用功能数据分析技术研究中文手写样本，找到能很好重建书写轨迹的二阶线性微分方程，并用该模型成功区分不同人的笔迹，分类准确率达100%。
Abstract Functional data analysis techniques are used to analyze a sample of handwriting in Chinese. The goals are (a) to identify a differential equation that satisfactorily models the data's dynamics, and (b) to use the model to classify handwriting samples taken from differential individuals. After preliminary smoothing and registration steps, a second-order linear differential equation, for which the forcing function is small, is found to provide a good reconstruction of the original script records. The equation is also able to capture a substantial amount of the variation in the scripts across replication. The cross-validated classification process is 100% effective for the samples analyzed.