Handwriting posture prediction based on unsupervised model

Abstract

Writing is an important basic skill for humans. To acquire such a skill, pupils often have to practice writing for several hours each day. However, different pupils usually possess distinct writing postures. Bad postures not only affect the speed and quality of writing, but also severely harm the healthy development of pupils’ spine and eyesight. Therefore, it is of key importance to identify or predict pupils’ writing postures and accordingly correct bad ones. In this paper, we formulate the problem of handwriting posture prediction for the first time. Further, we propose a neural network constructed with small convolution kernels to extract features from handwriting, and incorporate unsupervised learning and handwriting data analysis to predict writing postures. Extensive experiments reveal that our approach achieves an accuracy rate of 93.3%, which is significantly higher than the 76.67% accuracy of human experts.