Data Preprocessing
One of the primary challenges we encountered was the preprocessing of the data. Sign language videos often contain a large amount of background noise and irrelevant information, making it difficult to extract the relevant sign language gestures. We had to develop sophisticated algorithms to filter out the noise and focus on the key gestures.
Computational Challenges
The computational requirements for training the machine learning models were significant. We had to optimize our algorithms and utilize parallel processing techniques to ensure that the models could be trained efficiently.
Uneven Number of Frames per Video
Another challenge was the uneven number of frames per video. Some videos contained more frames than others, which could bias the training process. We had to develop techniques to handle this imbalance and ensure that the models were trained on a representative dataset.
System Crashing While Training
During the training process, we encountered issues with system crashes due to the high computational load. We had to implement robust error handling mechanisms and optimize our code to prevent these crashes and ensure that the training process could continue uninterrupted.
Overall, these challenges required us to develop innovative solutions and leverage advanced techniques to ensure the success of the project. Through perseverance and dedication, we were able to overcome these obstacles and create a robust and accurate sign language recognition system.