The Effect of Color Combinations on the Efficiency of Text Recognition in Digital Devices

Abstract

The color combination of the text background in digital reading can significantly impact reading efficiency, and prolonged digital reading can affect the efficiency of text recognition. Text background color combinations designed for the display characteristics of digital devices can make users’ reading more efficient. This study investigated the effects of five factors - hue, saturation, brightness, text color and background texture - on text recognition efficiency and text legibility in digital reading, using eye-tracking technology and the E-prime psychology experimental operating platform. Pupil diameter was recorded using eye-tracking technology, and correctness and response time were recorded on the E-prime psychological operating platform, with subjective assessment of text legibility on a Likert scale. The results showed that the green-black combination had better legibility and higher recognition efficiency among light background and dark text combinations. And the peach-light grey combination performed the worst. As for the dark background and light text combinations, the green-white combination showed better legibility and recognition efficiency. Meanwhile, the background texture did not significantly affect reading efficiency in the experiment. This study investigated the factors influencing digital reading interfaces regarding recognition efficiency and legibility, which provided a reference for the design of digital reading interactive interfaces and had practical implications for reducing recognition efficiency decline due to prolonged reading.

View correction statement:

Correction

Ethical approval

This information is available upon request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Shandong Province Professional Degree Graduate Teaching Case Library Construction Project (SDYAL19008) and Shandong University Education and teaching reform research project (No. 2020Y218).

Notes on contributors

Heng Guo

Heng Guo received her master’s degree in mechanical engineering from Shandong University in 2022. Her research interests include user experience research and human-computer interaction. She was awarded the Academic Scholarship of Shandong University.

Weihao Wang

Weihao Wang received his master’s degree in mechanical engineering from Shandong University in 2022. He is currently studying for his PhD at the School of Mechanical Engineering, Shandong University, China. His research interests include color planning, human-computer interaction and barrier-free design.

Fanghao Song

Fanghao Song is currently a Professor and a PhD Supervisor with the School of Mechanical Engineering, Shandong University, and the Director of the Institute of Modern Industrial Design. His main research interests include design cognition, design anthropology, and design aesthetics.

Yan Liu

Yan Liu is currently a Professor and a PhD Supervisor with the School of Mechanical Engineering, Shandong University, and the Director of the Department of Industrial Design, School of Mechanical Engineering. Her main research interests include design cognition, design anthropology, and design aesthetics.

Hui Liu

Hui Liu received her master’s degree in mechanical engineering from Shandong University in 2022. Her research interests include user experience research and human-computer interaction. She was awarded the Academic Scholarship of Shandong University.

Zhenming Bo

Zhenming Bo received his master’s degree in Mechanical Engineering, Shandong University, in 2022. His research interests include user experience research, human-computer interaction and virtual reality interaction design. He was awarded the Academic Scholarship of Shandong University.