# AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting

arXiv: https://arxiv.org/abs/2008.00714 · HTML: https://mlsanity.com/paper/2008.00714/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 14 of 14)

- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition](https://mlsanity.com/paper/2203.10209/) (2022)
- [When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding](https://mlsanity.com/paper/2506.05551/) (2025)
- [Arbitrary Reading Order Scene Text Spotter with Local Semantics Guidance](https://mlsanity.com/paper/2412.10159/) (2024)
- [MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression](https://mlsanity.com/paper/2307.07662/) (2023)
- [DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting](https://mlsanity.com/paper/2305.19957/) (2023)
- [SwinTextSpotter v2: Towards Better Synergy for Scene Text Spotting](https://mlsanity.com/paper/2401.07641/) (2024)
- [TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model](https://mlsanity.com/paper/2403.10047/) (2024)
- [LRANet++: Low-Rank Approximation Network for Accurate and Efficient Text Spotting](https://mlsanity.com/paper/2511.05818/) (2025)
- [TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision](https://mlsanity.com/paper/2306.03377/) (2023)
- [Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance](https://mlsanity.com/paper/2310.00917/) (2023)
- [GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding](https://mlsanity.com/paper/2105.04650/) (2021)
- [Deformation Robust Text Spotting with Geometric Prior](https://mlsanity.com/paper/2308.16404/) (2023)
- [HIP: Hierarchical Point Modeling and Pre-training for Visual Information Extraction](https://mlsanity.com/paper/2411.01139/) (2024)

## Abstract (arXiv)

Scene text spotting aims to detect and recognize the entire word or sentence with multiple characters in natural images. It is still challenging because ambiguity often occurs when the spacing between characters is large or the characters are evenly spread in multiple rows and columns, making many visually plausible groupings of the characters (e.g. "BERLIN" is incorrectly detected as "BERL" and "IN" in Fig. 1(c)). Unlike previous works that merely employed visual features for text detection, this work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both visual and linguistic features to significantly reduce ambiguity in text detection. The proposed AE TextSpotter has three important benefits. 1) The linguistic representation is learned together with the visual representation in a framework. To our knowledge, it is the first time to improve text detection by using a language model. 2) A carefully designed language module is utilized to reduce the detection confidence of incorrect text lines, making them easily pruned in the detection stage. 3) Extensive experiments show that AE TextSpotter outperforms other state-of-the-art methods by a large margin. For example, we carefully select a validation set of extremely ambiguous samples from the IC19-ReCTS dataset, where our approach surpasses other methods by more than 4%. The code has been released at https://github.com/whai362/AE_TextSpotter. The image list and evaluation scripts of the validation set have been released at https://github.com/whai362/TDA-ReCTS.