# ADDSL: Hand Gesture Detection and Sign Language Recognition on Annotated Danish Sign Language

arXiv: https://arxiv.org/abs/2305.09736 · HTML: https://mlsanity.com/paper/2305.09736/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 2 of 2)

- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Classification of Phonological Parameters in Sign Languages](https://mlsanity.com/paper/2205.12072/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [Training Strategies for Isolated Sign Language Recognition](https://mlsanity.com/paper/2412.11553/) (2024)
- [A HamNoSys-Guided Dataset and Baselines for Fine-Grained Isolated Handshape Recognition in Sign Language](https://mlsanity.com/paper/2608.10588/) (2026)

## Abstract (arXiv)

For a long time, detecting hand gestures and recognizing them as letters or numbers has been a challenging task. This creates communication barriers for individuals with disabilities. This paper introduces a new dataset, the Annotated Dataset for Danish Sign Language (ADDSL). Annota-tions for the dataset were made using the open-source tool LabelImg in the YOLO format. Using this dataset, a one-stage ob-ject detector model (YOLOv5) was trained with the CSP-DarkNet53 backbone and YOLOv3 head to recognize letters (A-Z) and numbers (0-9) using only seven unique images per class (without augmen-tation). Five models were trained with 350 epochs, resulting in an average inference time of 9.02ms per image and a best accu-racy of 92% when compared to previous research. Our results show that modified model is efficient and more accurate than existing work in the same field. The code repository for our model is available at the GitHub repository https://github.com/s4nyam/pvt-addsl.