  We study a novel multimodal-learning problem, which we call text matching:
given an image containing a single-line text and a candidate text
transcription, the goal is to assess whether the text represented in the image
corresponds to the candidate text. We devise the first machine-learning model
specifically designed for this problem. The proposed model, termed TextMatcher,
compares the two inputs by applying a cross-attention mechanism over the
embedding representations of image and text, and it is trained in an end-to-end
fashion. We extensively evaluate the empirical performance of TextMatcher on
the popular IAM dataset. Results attest that, compared to a baseline and
existing models designed for related problems, TextMatcher achieves higher
performance on a variety of configurations, while at the same time running
faster at inference time. We also showcase TextMatcher in a real-world
application scenario concerning the automatic processing of bank cheques.
