Abstract
Indian sign language (ISL) is used by deaf people in India for their internal communication. Sign language recognition (SLR) system recognizes gestures of sign language and converts them into text and voice, thus enabling deaf people to communicate with others. In this paper, we have presented an automatic system for ISL hand gesture recognition. Modern features like Histogram of Oriented Gradient (HOG), Speeded Up Robust Features (SURF), Fourier Descriptors, Hu Moments, and Zernike Moments have been extracted from training images of our own created dataset. Using these features, most commonly used classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) have been trained and evaluated. Comparative performance analysis of these classifiers along with experimental results is the main topic of discussion included in this paper. Moreover, a user-friendly GUI of the system allows user to select any combination of features and classifiers to perform gesture recognition from both stored file and live camera. During experiment, it is found that the system is able to recognize gestures with a recognition rate up to 98.70%.
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Patel, P., Patel, N. (2021). Comparison of Various Classifiers for Indian Sign Language Recognition Using State of the Art Features. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1334. Springer, Singapore. https://doi.org/10.1007/978-981-33-6981-8_55
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