The utilization of robots to mimic human calligraphic behavior and creation has long intrigued researchers in the field of robotics, but the performance of current calligraphy robots in producing satisfactory results is subpar. In this paper, we present a robotic calligraphy system comprising stroke processing and trajectory planning modules. The system takes a text image as input, sourced from either calligraphic copybook or field writing by the calligrapher. An image processing algorithm, coupled with a Convolutional Neural Network (CNN), is employed to segment characters and recognize strokes. Following stroke skeletonization, the resultant skeleton image comprises multiple path points. Subsequently, we integrate path points with stroke writing conventions, employ a dynamic path planning method to automatically generate the sequence of trajectory points, which is used to control the robot motion trajectory. Experimental validation confirms the feasibility of the proposed system, suggesting its potential as an alternative method for robotic calligraphy reproduction in practical applicationsd.