# Challenges for Linguistically-Driven Computer-Based Sign Recognition from Continuous Signing for American Sign Language

arXiv: https://arxiv.org/abs/2311.00762 · HTML: https://mlsanity.com/paper/2311.00762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 64 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)

## Built on by (most cited first, 1 of 1)

- [MHB: Multimodal Handshape-aware Boundary Detection for Continuous Sign Language Recognition](https://mlsanity.com/paper/2511.19907/) (2025)

## Abstract (arXiv)

There have been recent advances in computer-based recognition of isolated, citation-form signs from video. There are many challenges for such a task, not least the naturally occurring inter- and intra- signer synchronic variation in sign production, including sociolinguistic variation in the realization of certain signs. However, there are several significant factors that make recognition of signs from continuous signing an even more difficult problem. This article presents an overview of such challenges, based in part on findings from a large corpus of linguistically annotated video data for American Sign Language (ASL). Some linguistic regularities in the structure of signs that can boost handshape and sign recognition are also discussed.