Exploring Sign Language Recognition techniques with Machine Learning

The exploration into sign language recognition, particularly Indian Sign Language (ISL), reveals various approaches and challenges in developing effective models for converting gestures into spoken language. The research, published in the International Journal of Neural Computing and Applications, highlights the complexity of ISL due to its two-handed gestures, which can lead to feature occlusion, making traditional computer vision techniques less effective. To address this, the study utilized multiple machine learning methods, including hardware-based, machine learning-based, and neural network-based techniques, such as a pre-trained VGG16 model with transfer learning, a deep convolutional neural network (DCNN), and a hierarchical neural network model. The latter showed significant improvements by using a segmented approach to classify images into one-handed or two-handed gestures with an SVM, followed by specialized neural networks for each type. The hierarchical model achieved high accuracy rates, demonstrating its potential to enhance communication between sign language users and non-users by effectively translating gestures into words.

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