# Towards an AI-Driven Video-Based American Sign Language Dictionary: Exploring Design and Usage Experience with Learners

arXiv: https://arxiv.org/abs/2504.05857 · HTML: https://mlsanity.com/paper/2504.05857/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [Towards Privacy-Aware Sign Language Translation at Scale](https://mlsanity.com/paper/2402.09611/) (2024)
- [ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition](https://mlsanity.com/paper/2304.05934/) (2023)
- [Wireframe-Based UI Design Search Through Image Autoencoder](https://mlsanity.com/paper/2103.07085/) (2021)
- [User Driven Model Adjustment via Boolean Rule Explanations](https://mlsanity.com/paper/2203.15071/) (2022)
- [QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration](https://mlsanity.com/paper/2403.15667/) (2024)
- [Recognizing American Sign Language Nonmanual Signal Grammar Errors in Continuous Videos](https://mlsanity.com/paper/2005.00253/) (2020)

## Abstract (arXiv)

Searching for unfamiliar American Sign Language (ASL) signs is challenging for learners because, unlike spoken languages, they cannot type a text-based query to look up an unfamiliar sign. Advances in isolated sign recognition have enabled the creation of video-based dictionaries, allowing users to submit a video and receive a list of the closest matching signs. Previous HCI research using Wizard-of-Oz prototypes has explored interface designs for ASL dictionaries. Building on these studies, we incorporate their design recommendations and leverage state-of-the-art sign-recognition technology to develop an automated video-based dictionary. We also present findings from an observational study with twelve novice ASL learners who used this dictionary during video-comprehension and question-answering tasks. Our results address human-AI interaction challenges not covered in previous WoZ research, including recording and resubmitting signs, unpredictable outputs, system latency, and privacy concerns. These insights offer guidance for designing and deploying video-based ASL dictionary systems.