Identification of Air-Writing Tamil Alphabetical Vowel Characters

Abstract

In recent years, there has been a lot of focus on gesture recognition because of its potential as a means of communication for cutting-edge gadgets. As a special category of gesture recognition, air-writing is the practice of forming letters or words in the air using one's fingers or the movements of one's hands. The primary objective of this study is to propose a classification framework with feature extraction techniques to enhance the recognition of vowel characters in the Tamil language. The data collection and classification procedure involved a set of 12 distinct letters. A methodology has been developed to facilitate the analysis of various configurations for the purpose of evaluation. To get useful features from the 2-second time window data segments, this study uses a one-dimensional convolutional neural network (1D CNN). In our approach, we employ five machine-learning methods to conduct our evaluation. These methods include Naive Bayes, Random Forest, K-Nearest Neighbor, Support Vector Machine, and Decision Tree. The classification algorithms are considered to be superior based on the results obtained from our dataset in this experiment. The results of the tests show that the suggested K-nearest neighbors (KNN) algorithm works very well when used with a k-1 and 0.6:0.4 split ratio for training and testing. Specifically, the KNN model achieved an accuracy rate of 91.67%. The present study builds upon previous research by utilizing applications that have been employed in prior studies. However, a unique aspect of our system is the integration of cutting-edge technology, which utilizes collected sensor data to classify the characters. The examination of the window size has the potential to enhance accuracy and performance.