Importance Estimation for Scene Texts Using Visual Features
Kota OODAIRA, Tomo Miyazaki, Yoshihiro Sugaya, Shinichiro Omachi · Interdisciplinary Information Sciences · 2022
In this study, we addressed the challenge of estimating the importance of texts in scene images. Research on text analysis in scene images has focused on detection and recognition; however, estimating its importance has not received much attention. We focused on the possibility that importance can be estimated from visual appearance. Therefore, in this study, we constructed scene image datasets, including texts, and assigned an importance to each text via subjective evaluation. Based on the subjective evaluation, the image features representing importance of text contents were determined, and an importance estimation model is proposed. The results of the evaluation experiment indicate that the proposed method can estimate the importance with a higher accuracy than the existing method.