# Recognising BSL Fingerspelling in Continuous Signing Sequences

arXiv: https://arxiv.org/abs/2603.19523 · HTML: https://mlsanity.com/paper/2603.19523/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 19 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Reconstructing Hands in 3D with Transformers](https://mlsanity.com/paper/2312.05251/) (2023)
- [Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels](https://mlsanity.com/paper/2303.14307/) (2023)
- [BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues](https://mlsanity.com/paper/2007.12131/) (2020)
- [Fingerspelling recognition in the wild with iterative visual attention](https://mlsanity.com/paper/1908.10546/) (2019)
- [A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision](https://mlsanity.com/paper/2405.10266/) (2024)
- [Weakly-supervised Fingerspelling Recognition in British Sign Language Videos](https://mlsanity.com/paper/2211.08954/) (2022)
- [Hands-On: Segmenting Individual Signs from Continuous Sequences](https://mlsanity.com/paper/2504.08593/) (2025)
- [Slovo: Russian Sign Language Dataset](https://mlsanity.com/paper/2305.14527/) (2023)
- [FSboard: Over 3 million characters of ASL fingerspelling collected via smartphones](https://mlsanity.com/paper/2407.15806/) (2024)
- [Fingerspelling PoseNet: Enhancing Fingerspelling Translation with Pose-Based Transformer Models](https://mlsanity.com/paper/2311.12128/) (2023)
- [One-Stage-TFS: Thai One-Stage Fingerspelling Dataset for Fingerspelling Recognition Frameworks](https://mlsanity.com/paper/2411.02768/) (2024)

## Abstract (arXiv)

Fingerspelling is a critical component of British Sign Language (BSL), used to spell proper names, technical terms, and words that lack established lexical signs. Fingerspelling recognition is challenging due to the rapid pace of signing and common letter omissions by native signers, while existing BSL fingerspelling datasets are either small in scale or temporally and letter-wise inaccurate. In this work, we introduce a new large-scale BSL fingerspelling dataset, FS23K, constructed using an iterative annotation framework. In addition, we propose a fingerspelling recognition model that explicitly accounts for bi-manual interactions and mouthing cues. As a result, with refined annotations, our approach halves the character error rate (CER) compared to the prior state of the art on fingerspelling recognition. These findings demonstrate the effectiveness of our method and highlight its potential to support future research in sign language understanding and scalable, automated annotation pipelines. The project page can be found at https://taeinkwon.com/projects/fs23k/.