EsharaGAN: An Approach to Generate Disentangle Representation of Sign Language using InfoGAN

EsharaGAN is a Bangla Sign Digit generation model based on Information Maximizing Generative Adversarial Networks (InfoGAN). Augmenting the mutual information between latent variables and observational variables is the fundamental working principle of InfoGAN. This paper focused on generating disentangle representation of Bangla Sign Digit images using a variant of Generative adversarial network InfoGAN. Working on the IsharaLipi dataset, this model consists of 13 layer network architecture-input layer, dense layer, convolutional layer, transpose convolutional layer, activation and batch normalization layer which minimizes the loss function, computation power and generates non distorted images like the real ones. ReLU and Tanh is used as an activation function. This model provides an exceptional result as the inception score of the model is 8.77 which is remarkable for a generation model.