Extensive research has shown that cognitive style is a non-negligible potential influencer of domains of human functioning, such as learning, creativity, and cooperation among individuals. However, the dichotomy of cognitive style is contradictory to the fact that cognitive style is a continuous variable, and the dichotomy loses information about the strength of people's performance between the poles of cognitive style. To solve this problem, this study developed a computerized continuous scoring system (CCS) based on Python's OpenCV library, and achieved continuous scoring of the test of cognitive style, with the Embedded Figure Test as an example. An empirical study was implemented to compare the performance of dichotomous scoring and CCS. The results show that CCS can accurately extract the traces of participants' responses and achieve continuous scoring, supplementing the information on the strength of people's cognitive styles between the two poles, and the performance of CCS-based tests such as discrimination, reliability, and validity are significantly improved compared with the dichotomous scoring. Given the high reproducibility of CCS, it is expected to be applied to scoring other continuity characteristics in the future.