Abstract
Pages on digital platforms used for online advertising in order to attract customer attention for a target product are called landing pages. The aim of landing pages is to increase advertisement conversion rates using metrics like clicks, views or subscriptions. In this study, a method is presented to automatically detect the most commonly used components on landing pages; buttons, texts and checkboxes. Landing page images given as inputs, are segmented by morphological and thresholding-based image analysis methods, and each segment is classified using Convolutional Neural Networks (CNN). The proposed method is anticipated to be an important step in the process of automatically designing landing pages with high advertisement conversion rates by segmenting pages into components that have higher performance metrics. In preliminary experiments, high accuracy is achieved in the test data set.
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