Experimental Measurement and Markov Chain Modelling of Stroke Order Intuition

Abstract

As a feature of Chinese characters, stroke order plays an important role in Chinese character education, Chinese character recognition and handwriting identification. However, in recent years, the research on stroke order is limited to elaboration and supplementation of existing stroke order and stroke order rules. There is no special research that focuses on the principle behind stroke order. To quantify the internal regularity of stroke order, we start with the stroke order intuition in the actual writing. Because stroke order has individual differences, we select a group of students with small differences as testers. In this paper, we design an algorithm to generate random strokes and collect the timing data during testers copying strokes. After pre-processing the collected data, a Markov chain is introduced to model stroke order intuition, which can divide the stroke sequences into three states. And then we classify them into three situations according to the stroke distribution. Next, we compare them with some well-known stroke order rules. The results show that the probability distribution of strokes in different situations is not always consistent with the empirical rules, as well as the relations between them.