ADDSL: Hand Gesture Detection and Sign Language Recognition on Annotated Danish Sign Language
This work addresses communication barriers for individuals with disabilities by improving sign language recognition, though it is incremental as it builds on existing object detection methods.
The paper tackled hand gesture detection and sign language recognition by introducing the Annotated Dataset for Danish Sign Language (ADDSL) and training a YOLOv5-based model, achieving a best accuracy of 92% with an average inference time of 9.02ms per image.
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.