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A Deep Learning-Based Approach to Classification of Baby Sign Language Images
Abstract
Baby Sign Language is used by hearing parents to hearing infants as a preverbal communication which reduce frustration of parents and accelerated learning in babies, increases parent-child bonding, and lets babies communicate vital information, such as if they are hurt or hungry is known as a Baby Sign Language . In the current research work, a study of various existing sign language has been carried out as literature and then after realizing that there is no dataset available for Baby Sign Language, we have created a static dataset for 311 baby signs, which were classified using a MobileNet V1, pretrained Convolution Neural Network [CNN].The focus of the paper is to analyze the effect of Gradient Descent based optimizers, Adam and its variants, Rmsprop optimizers on fine-tuned pretrained CNN model MobileNet V1 that has been trained using customized dataset. The optimizers are used to train and test on MobileNet for 100 epochs on the dataset created for 311 baby Signs. These 10 optimizers Adadelta, Adam, Adamax, SGD, Adagrad, RMSProp were compared based on their processing time.
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