Advertisement Image Classification—Visual ( <scp>RESNET</scp> ) Versus Textual ( <scp>BERT</scp> ) Features: An Experimental Study

ABSTRACT Newspapers serve as a vital source for various types of advertisements. Individuals eagerly await and search for advertisements relevant to them in newspapers. However, both printed newspapers and online newspapers lack the ability to provide category‐wise advertisement search options. As a result, searching a newspaper advertisement in a specific category becomes very time‐consuming and cumbersome due to sequential manual search across multiple newspapers. To address this problem in online newspapers, a classification model is needed that can classify advertisement images into predefined categories and hence allow users to perform category‐wise advertisement searches with much ease. This research introduces and compares two sets of classification models for advertisement images in online English newspapers in India. The first set utilizes visual features to train seven different classification models by fine‐tuning different layers of the Residual Network with 50 layers (ResNet50) pretrained model and achieves a maximum classification accuracy of 71.41%. The second set utilizes textual features to train 14 different classification models by fine‐tuning different layers of the pretrained Bidirectional Encoder Representations from Transformers (BERT) base model and achieves maximum classification accuracies in the range from 96.88% to 97.34%. This significant enhancement of more than 25% underscores the superiority of textual features over visual ones in understanding Indian online English newspaper advertisement images and holds promise for practical applications, including categorized advertisement searches.