Efficient sign language recognition system and dataset creation method based on deep learning and image processing

Abstract

New deep-learning architectures are created every year, achieving state-of-the-art results in image recognition and leading to the belief that, in a few years, complex tasks such as sign language translation will be considerably easier, serving as a communication tool for the hearing-impaired community. On the other hand, these algorithms still need a lot of data to be trained and the dataset creation process is expensive, time-consuming, and slow. Thereby, this work aims to investigate techniques of digital image processing and machine learning that can be used to create a sign language dataset effectively. We argue about data acquisition, such as the frames per second rate to capture or subsample the videos, the background type, preprocessing, and data augmentation, using convolutional neural networks and object detection to create an image classifier and comparing the results based on statistical tests. Different datasets were created to test the hypotheses, containing 14 words used daily and recorded by different smartphones in the RGB color system. We achieved an accuracy of 96.38% on the test set and 81.36% on the validation set containing more challenging conditions, showing that 30 FPS is the best frame rate subsample to train the classifier, geometric transformations work better than intensity transformations, and artificial background creation is not effective to model generalization. These trade-offs should be considered in future work as a cost-benefit guideline between computational cost and accuracy gain when creating a dataset and training a sign recognition model.

Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition

“…Based on such benchmarks, numerous CSLR studies have been proposed [5,7,20,28,37,41,44,57,61]. In isolated SLR tasks, Carneiro et al [6] leverage background replacement for data augmentation at train-time. However, robustness to background shift has not been explored in the CSLR field [11,28,35].…”

“…3(b), we obtain a convex sum [60] of the target video with random background images. While Carneiro et al [6] use person masks for changing backgrounds in training ISLR models, we emphasize BR is done without masks to reduce additional labor costs.…”