# Text Recognition -- Real World Data and Where to Find Them

arXiv: https://arxiv.org/abs/2007.03098 · HTML: https://mlsanity.com/paper/2007.03098/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

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

- [What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels](https://mlsanity.com/paper/2103.04400/) (2021)
- [Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer](https://mlsanity.com/paper/2202.05508/) (2022)
- [Multimodal Semi-Supervised Learning for Text Recognition](https://mlsanity.com/paper/2205.03873/) (2022)
- [FEDS -- Filtered Edit Distance Surrogate](https://mlsanity.com/paper/2103.04635/) (2021)
- [Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition](https://mlsanity.com/paper/2410.09913/) (2024)

## Abstract (arXiv)

We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The proposed method includes matching of imprecise transcription to weak annotations and edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as "pseudo ground truth" (PGT). We apply the method to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7~% on average, across different benchmark datasets (image domains) and 24.5~% on one of the weakly annotated datasets.