Handwritten Chinese Character Guidance System

With the general trend of informatization, the importance of handwritten Chinese characters is gradually being ignored. Due to the differences in the education levels and the writing habits of the public, problems such as confusion of stroke order, distorted fonts, and wrong postures for holding the pen always exist. Whether from the perspective of cultural inheritance or practicality, the learning of handwritten Chinese characters is an indispensable and important link in contemporary education. In this project, we hope that by introducing artificial intelligence and computer vision technology, we can monitor and guide students’ writing ability from the two aspects of pen grip posture and stroke order detection. The VGG16 Model was utilized to implement the function of pen grip posture classification. For the recognition of stroke order, it is hoped that a single Chinese character can be recognized first through real-time pen writing trajectories, so as to further perform the stroke extraction. CRNN (Convolutional Recurrent Neural Network) + CTPN (Connectionist Text Proposal Network) were applied for Handwriting Chinese characters recognition. SiamRPN++ model was implemented to track the pen tip region in the writing video, and through pen ballpoint detection and pen up/down classification, to achieve stroke segmentation of a single character. While in the last algorithm, the strokes would be classified as horizontal, vertical, oblique upwards, and oblique downwards according to the absolute value of the slope, and a length threshold and finally achieve the correctness judgment.