# Single Shot Scene Text Retrieval

arXiv: https://arxiv.org/abs/1808.09044 · HTML: https://mlsanity.com/paper/1808.09044/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2018.

## Built on by (most cited first, 11 of 11)

- [SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text](https://mlsanity.com/paper/2204.11964/) (2022)
- [ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval](https://mlsanity.com/paper/2203.16778/) (2022)
- [Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement](https://mlsanity.com/paper/2203.04814/) (2022)
- [Scene Text Retrieval via Joint Text Detection and Similarity Learning](https://mlsanity.com/paper/2104.01552/) (2021)
- [A Comprehensive Survey on Visual Question Answering Datasets and Algorithms](https://mlsanity.com/paper/2411.11150/) (2024)
- [Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval](https://mlsanity.com/paper/2408.00441/) (2024)
- [Self-Supervised Learning for Text Recognition: A Critical Survey](https://mlsanity.com/paper/2407.19889/) (2024)
- [Partial Scene Text Retrieval](https://mlsanity.com/paper/2411.10261/) (2024)
- [Domain Adaptive Scene Text Detection via Subcategorization](https://mlsanity.com/paper/2212.00377/) (2022)
- [Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages](https://mlsanity.com/paper/2111.12276/) (2021)
- [MSTAR: Box-free Multi-query Scene Text Retrieval with Attention Recycling](https://mlsanity.com/paper/2506.10609/) (2025)

## Abstract (arXiv)

Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image database. Our experiments demonstrate that the proposed architecture outperforms previous state-of-the-art while it offers a significant increase in processing speed.