Gradient Based Key Frame Extraction for Continuous Indian Sign Language Gesture Recognition and Sentence Formation in Kannada Language: A Comparative Study of Classifiers - Ramesh M, Kagalkar; Gumaste, SV (International Journal of Computer Sciences and Engineering, 2025)
Human hands are delicate instruments. Hand gestures and finger gestures are excellent ways of emphasizing what we say, but on the other hand they can also reveal our true intentions. In this paper introduced a continuous Indian sign language recognition system, wherever each the hands are used for playacting any gesture. Recognizing a sign language gestures from continuous gestures could be a terribly difficult analysis issue. This paper solve the problem using gradient based key frame extraction technique. These key frames are useful for splitting continuous language gestures into sequence of signs further as for removing uninformative frames. After splitting of gestures every sign has been treated as associate degree isolated gesture. Then features of pre-processed gestures are extracted using orientation histogram (OH) with principal component analysis (PCA) is applied for reducing dimension of features obtained after OH. Experiments are performed on our own continuous ISL dataset which is created using EOS camera in PG Research Laboratory (SPPU, Pune). Probes are tested exploitation varied forms of classifiers like, Manhattan distance, Correlation, Manhattan distance, City block distance, Euclidian distance etc. Comparative analysis of our projected theme is performed with varied forms of distance classifiers. From this analysis we tend to found that the results obtained from Correlation and Euclidian distance offers higher accuracy then alternative classifiers.