Sign Language Recognition using Machine Learning Approach

Abstract

In the world of sign language, and hand gestures, a lot of research work has been done over the past three decades. Woefully, every research has its own limitations and are still unable to be used commercially. The main problem of this way of communication is normal people who cannot understand sign language can't communicate with these people or vice versa. Many researches have known to be successful for recognizing sign language, but require an expensive cost to be commercialized. Researchers do their researches in various ways. It starts from the data acquisition methods. The data acquisition method changes because of the cost needed for a good device, but cheap method is needed for the Sign Language Recognition System to be commercialized. The method used in developing Sign Language Recognition also varies between researchers. Each method has its own strength compare to other methods and many researchers are still using different methods in developing their own Sign Language Recognition.

The Comparison of K-Nearest Neighbors and Random Forest Algorithm to Recognize Indonesian Sign Language in a Real-Time

“…In previous researches, according to Taunk et al in [32], K-Nearest Neighbor (K-NN) algorithm is a machine learning algorithm that is non-parametric and a lazy learning algorithm [11][14] [19][26] [36], which means that the algorithm does not make assumptions about the distribution of the underlying data, it does not use training data to create a model, but only stores and remembers the training data, so it can be said to be lighter in terms of computing load and time [10][11][19] [33] [36]. Meanwhile, the Convolutional Neural Network (CNN) method was known less effective due to the heavy computational load or time [7][24] [31].…”