  We present CONSENT, a simple yet effective CONtext SENsitive Transformer
framework for context-dependent object classification within a fully-trainable
end-to-end deep learning pipeline. We exemplify the proposed framework on the
task of bold words detection proving state-of-the-art results. Given an image
containing text of unknown font-types (e.g. Arial, Calibri, Helvetica), unknown
language, taken under various degrees of illumination, angle distortion and
scale variation, we extract all the words and learn a context-dependent binary
classification (i.e. bold versus non-bold) using an end-to-end
transformer-based neural network ensemble. To prove the extensibility of our
framework, we demonstrate competitive results against state-of-the-art for the
game of rock-paper-scissors by training the model to determine the winner given
a sequence with $2$ pictures depicting hand poses.
