Chinese fonts with smooth outlines and solid colouring have been produced for computer displays and printings for a long time. However, the aesthetic properties of the characters produced by calligraphers could not be simulated with these methods. Ip and Wong proposed a parameterised brush model that enables efficient generation of Chinese calligraphic writings such that the rendering is scalable in resolution and it allows high quality publishing. While this graphical model facilitates the synthesis of calligraphy writing given a set of writing parameters, for the inverse problem, a lot of user stroke manipulation is required to regenerate the model parameters given a calligraphic image. Consequently, an intelligent method is required to automatically determine the model parameters from images of Chinese calligraphy. This paper describes a methodology for automatically estimating the set of 3-D geometric and dynamic writing parameters along a stroke trajectory from images of calligraphic writings.