TextMatcher: Cross-Attentional Neural Network to Compare Image and Text

• URL: http://arxiv.org/abs/2205.05507v1
• Date: Wed, 11 May 2022 14:01:12 GMT
• Title: TextMatcher: Cross-Attentional Neural Network to Compare Image and Text
• Authors: Valentina Arrigoni, Luisa Repele, Dario Marino Saccavino
• Abstract summary: We devise the first machine-learning model specifically designed for this problem. We extensively evaluate the empirical performance of TextMatcher on the popular IAM dataset. We showcase TextMatcher in a real-world application scenario concerning the automatic processing of bank cheques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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