In visual media production (e.g., in marketing and film industry), it is important that the text on images and video is legible regardless of the background. The goal of the thesis is to develop and evaluate a method to determine the legibility of text on arbitrary backgrounds. The dataset was created using surveys. For a large dataset of photos, we asked the participants whether they are legible or not. Subsequently, we gathered key features (contrast, lightness etc.) by using the RGB and HSL color models. The gathered data were employed to build a linear model. Because we perceive legibility as binary, we used logistic regression. The model was evaluated using such methods as AUC and cross validation. The final classification model is 68% accurate at predicting legibility. Based on these results, advertisers can, from a set of generated ads, select the most legible.
|
