Indian Sign Language Recognition for Static and Dynamic Hand Gestures - Prajapati, Manav; Makawana, Mitesh; Hada, Sahil (International Journal of Computer Sciences and Engineering, 2020)
Humans are called as social animals and because of that communication becomes a very integral part of a human being. Humans use verbal and non-verbal forms of speech for communication purposes, but not all humans are capable of verbal speech, for e.g. Deaf and Mute people. Hence, Sign Languages are developed for them, but still there is a hindrance in the communication for them. So, using the hand gestures, this paper presents a system where CNN network is used to for the classification of Alphabets and Numbers. CNN is used because alphabets and number gestures are static gestures in Indian Sign Language and CNNs give very good results for image classification. This uses hand-masked (skin- segmentation) images for training the model. For the dynamic hand gestures, the system uses LSTM network for the classification task. LSTM are well known for accurately predicting the data which is distributed in time-frame. This paper presents two models, CNN and LSTM for predicting different type of hand gestures i.e. static as well as dynamic.