# Text Recognition in the Wild: A Survey

arXiv: https://arxiv.org/abs/2005.03492 · HTML: https://mlsanity.com/paper/2005.03492/

## Key facts

- Cited by 29 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 29 of 29)

- [DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting](https://mlsanity.com/paper/2211.10772/) (2022)
- [Conditional Text Image Generation with Diffusion Models](https://mlsanity.com/paper/2306.10804/) (2023)
- [CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition](https://mlsanity.com/paper/2111.11011/) (2021)
- [Multi-Granularity Prediction for Scene Text Recognition](https://mlsanity.com/paper/2209.03592/) (2022)
- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [Levenshtein OCR](https://mlsanity.com/paper/2209.03594/) (2022)
- [Multimodal Semi-Supervised Learning for Text Recognition](https://mlsanity.com/paper/2205.03873/) (2022)
- [DNTextSpotter: Arbitrary-Shaped Scene Text Spotting via Improved Denoising Training](https://mlsanity.com/paper/2408.00355/) (2024)
- [Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition](https://mlsanity.com/paper/2408.06352/) (2024)
- [Open-set Text Recognition via Character-Context Decoupling](https://mlsanity.com/paper/2204.05535/) (2022)
- [Multi-Granularity Prediction with Learnable Fusion for Scene Text Recognition](https://mlsanity.com/paper/2307.13244/) (2023)
- [Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers](https://mlsanity.com/paper/2112.04934/) (2021)
- [TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/2207.06744/) (2022)
- [Data Augmentation for Scene Text Recognition](https://mlsanity.com/paper/2108.06949/) (2021)
- [OmniPrint: A Configurable Printed Character Synthesizer](https://mlsanity.com/paper/2201.06648/) (2022)
- [STRIDE : Scene Text Recognition In-Device](https://mlsanity.com/paper/2105.07795/) (2021)
- [NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2407.11380/) (2024)
- [Self-Supervised Learning for Text Recognition: A Critical Survey](https://mlsanity.com/paper/2407.19889/) (2024)
- [Improving Chinese Character Representation with Formation Tree](https://mlsanity.com/paper/2404.12693/) (2024)
- [IterVM: Iterative Vision Modeling Module for Scene Text Recognition](https://mlsanity.com/paper/2204.02630/) (2022)
- [The MERIT Dataset: Modelling and Efficiently Rendering Interpretable Transcripts](https://mlsanity.com/paper/2409.00447/) (2024)
- [ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment](https://mlsanity.com/paper/2107.05279/) (2021)
- [Ultra Light OCR Competition Technical Report](https://mlsanity.com/paper/2110.12623/) (2021)
- [Portmanteauing Features for Scene Text Recognition](https://mlsanity.com/paper/2211.05036/) (2022)
- [VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild](https://mlsanity.com/paper/2303.06048/) (2023)
- [HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution](https://mlsanity.com/paper/2307.16410/) (2023)
- [ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models](https://mlsanity.com/paper/2403.06586/) (2024)
- [AI-Powered Data Visualization Platform: An Intelligent Web Application for Automated Dataset Analysis](https://mlsanity.com/paper/2511.08363/) (2025)
- [The RealDefocus Benchmark for Defocus Deblurring](https://mlsanity.com/paper/2607.21078/) (2026)

## Abstract (arXiv)

The history of text can be traced back over thousands of years. Rich and precise semantic information carried by text is important in a wide range of vision-based application scenarios. Therefore, text recognition in natural scenes has been an active research field in computer vision and pattern recognition. In recent years, with the rise and development of deep learning, numerous methods have shown promising in terms of innovation, practicality, and efficiency. This paper aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition; (2) introduce new insights and ideas; (3) provide a comprehensive review of publicly available resources; (4) point out directions for future work. In summary, this literature review attempts to present the entire picture of the field of scene text recognition. It provides a comprehensive reference for people entering this field, and could be helpful to inspire future research. Related resources are available at our Github repository: https://github.com/HCIILAB/Scene-Text-Recognition.