Vision-based hand gesture recognition of alphabets, numbers, arithmetic operators and ASCII characters in order to develop a virtual text-entry interface system

Hand gesture recognition can substitute the use of text-entry interface for human computer interaction. However, it is a challenging task to develop a virtual text-entry interface covering a large number of gesture-based characters. In this paper, 18 new ASCII printable characters have been introduced along with some of the previously introduced characters [A–Z alphabets, 0–9 numbers and four arithmetic operators (add, minus, multiply, divide)]. In addition to some of the efficient existing features, three new features of 15 dimensions have been incorporated to enhance the performance of the system, which are normalized distance between direction extreme, close figure test and direction change ratio. These features are measured for single-stroke as well as multistroke gestures. An experimental analysis has been carried out for selection of optimal features using the statistical analysis techniques such as one-way analysis of variance test, Kruskal–Wallis test, Friedman test in combination with incremental feature selection technique. Furthermore, a comparative study has been carried out for classification of 58 gestures with the new list of features. A comparative analysis has been performed using five classifiers, namely SVM, kNN, Naive Bayes, ANN and ELM. It has been observed that maximum accuracy achieved using the combination of existing and proposed features is 96.95%, as compared to 94.60% accuracy achieved using existing features for classification of 58 gestures.