  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.
