Implementation and Comparison of Machine Learning Algorithms for Recognition of Fingerspelling in Indian Sign Language - Ramaswamy, Bhakthavathsalam; Aatrei M, Nikhil; H N, Shreyas; S. Iyer, Sumesh; K H, Gowranga (Transactions on Engineering and Computing Sciences, 2017)
Communication is the biggest hurdle faced by the hearing and speech impaired in leading a normal life. In this context, Sign Language is the most prominent means of communication. Machine learning and Computer Vision is an integral part of today’s computing world. This research paper proposes a Machine Learning based system to recognize fingerspelling gestures present in Indian Sign Language. Edge Frequency technique is chosen for Feature Extraction. The system was implemented using Aforge.NET framework. A comparative analysis of the Machine Learning Algorithms consisting of Support Vector Machine (SVM), K- Nearest Neighbor (KNN), Adaptive Naïve Bayes Classifier (ANBC), Decision Tree (DT) and Random Forests (RF) is performed to find out which algorithm is the most suitable to recognize ISL. Comparison is done based on validation accuracies and confusion matrices obtained. The accuracy for KNN was found to be 97.44% while SVM and ANBC have an accuracy of 96.15% and 82.05% respectively.