1. American Sign Language Alphabet Recognition
Connor Blazek

2. Summary Motivation Results
People with hearing or speech impairment often use sign language to communicate
Most people do not understand sign language
Results
Performed well within the dataset
Performed poorly outside of the dataset

3. Approach MobileNet Datasets found on Kaggle
Pre-trained
Transfer Learning
Ranks Images based on “confidence”
Changed input parameters and datasets
Datasets found on Kaggle
3000 images per sign
Achieved ~ 96% accuracy within dataset

4. Approach Added 2 smaller datasets
Now only 90 images per sign
Achieved ~ 85% accuracy within dataset
Better with other signs but still very bad

5. Results Learning Rate of 0.05 was usually the best
Low Learning Rates caused slower learning an a lower accuracy
High Rates only increase confidence

6. Results
Struggles with:
H, U
L, T, X
A, E, N (+ M, S) V

7. Discussion Lessons learned How to improve
Variety in dataset is important
Learning Rate of about 0.05 yields best accuracy
How to improve
Use more diverse dataset
Make sure overfitting has not taken place

8. References
Mekala, Priyanka & Gao, Ying & Fan, Jeffrey & Davari, A. (2011). Real-time sign language recognition based on neural network architecture. Proceedings of the Annual Southeastern Symposium on System Theory /SSST
Zee, S. (2018, September 20). Whose Sign Is It Anyway? AI Translates Sign Language Into Text. Retrieved October 7, 2018, from language/
Datasets: