# Text Detection and Recognition in the Wild: A Review

arXiv: https://arxiv.org/abs/2006.04305 · HTML: https://mlsanity.com/paper/2006.04305/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach](https://mlsanity.com/paper/2308.08806/) (2023)
- [Arbitrary Shape Text Detection using Transformers](https://mlsanity.com/paper/2202.11221/) (2022)
- [Detection Masking for Improved OCR on Noisy Documents](https://mlsanity.com/paper/2205.08257/) (2022)
- [Multimodal Transformer for Comics Text-Cloze](https://mlsanity.com/paper/2403.03719/) (2024)
- [Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer](https://mlsanity.com/paper/2404.12734/) (2024)
- [Text Detection & Recognition in the Wild for Robot Localization](https://mlsanity.com/paper/2205.08565/) (2022)
- [SAViL-Det: Semantic-Aware Vision-Language Model for Multi-Script Text Detection](https://mlsanity.com/paper/2507.20188/) (2025)
- [AIRRI: Towards a Human-Readable, Machine-Resistant Reading Interface](https://mlsanity.com/paper/2610.18724/) (2026)

## Abstract (arXiv)

Detection and recognition of text in natural images are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, autonomous driving, industrial automation, to name a few. They face common challenging problems that are factors in how text is represented and affected by several environmental conditions. The current state-of-the-art scene text detection and/or recognition methods have exploited the witnessed advancement in deep learning architectures and reported a superior accuracy on benchmark datasets when tackling multi-resolution and multi-oriented text. However, there are still several remaining challenges affecting text in the wild images that cause existing methods to underperform due to there models are not able to generalize to unseen data and the insufficient labeled data. Thus, unlike previous surveys in this field, the objectives of this survey are as follows: first, offering the reader not only a review on the recent advancement in scene text detection and recognition, but also presenting the results of conducting extensive experiments using a unified evaluation framework that assesses pre-trained models of the selected methods on challenging cases, and applies the same evaluation criteria on these techniques. Second, identifying several existing challenges for detecting or recognizing text in the wild images, namely, in-plane-rotation, multi-oriented and multi-resolution text, perspective distortion, illumination reflection, partial occlusion, complex fonts, and special characters. Finally, the paper also presents insight into the potential research directions in this field to address some of the mentioned challenges that are still encountering scene text detection and recognition techniques.