# Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding

## TL;DR

This paper introduces a joint approach combining visual features and scene text for improved image understanding, demonstrating enhanced retrieval and classification performance by modeling their interplay and applying attention mechanisms.

## Contribution

It presents a novel joint embedding of visual and scene text cues, incorporating their interaction and attention to improve semantic image interpretation.

## Key findings

- Enhanced retrieval accuracy using combined text-visual embeddings
- Improved classification performance over state-of-the-art methods
- Effective handling of erroneous scene text recognition through attention

## Abstract

Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art.