Sparse Dictionary Learning generates a sparse representation for images and signals along with a generalized learned dictionary. We examine closely to the constrained recurrent sparse auto-encoder (CRsAE) on its Encoder-Decoder plus recurrent architecture and experimenting CRsAE’s position in the classical dictionary learning problem. We further extend the visualizations, experiments, and metrics to evaluate the model in the context of both VAE and Dictionary Learning.