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

arXiv: https://arxiv.org/abs/1905.10622 · HTML: https://mlsanity.com/paper/1905.10622/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2019.

## Built on by (most cited first, 5 of 5)

- [Analyzing Online Political Advertisements](https://mlsanity.com/paper/2105.04047/) (2021)
- [Benchmarking VLMs' Reasoning About Persuasive Atypical Images](https://mlsanity.com/paper/2409.10719/) (2024)
- [Don't Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Models](https://mlsanity.com/paper/2405.20846/) (2024)
- [EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQA](https://mlsanity.com/paper/2108.09717/) (2021)
- [Weakly Supervised Scene Text Detection using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.04866/) (2022)

## Abstract (arXiv)

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.