The ¯eld of handwritten Chinese character recog- nition (HCCR) has seen signi¯cant advances in the last two decades, owing to the e®ectiveness of many techniques, especially those for character shape normalization and feature extraction. This paper reviews the major methods of normalization and feature extraction, and evaluates their perfor- mance experimentally. The normalization meth- ods include linear normalization, nonlinear normal- ization (NLN) based on line density equalization, moment normalization (MN), bi-moment normaliza- tion (BMN), modi¯ed centroid-boundary alignment (MCBA), and their pseudo-two-dimensional (pseudo 2D) extensions. As to feature extraction, we fo- cus on some e®ective variations of direction features: chaincode feature, normalization-cooperated chain- code feature (NCCF), and gradient feature. We have compared the normalization methods previ- ously, but in this study, will compare them with better implementation of features. As results, the current methods perform superiorly on handprinted characters, but are insu±cient for unconstrained handwriting.