Geometrical analysis of a shape through skeletonization has some of very important high- and low-level application which includes tracking, manipulation, retrieval, representation, registration, recognition, and compression. The task of skeletonization is defined as the generation of the medial axis of the shape while preserving its original topology and geometry. While the earlier approaches are mainly based on extracting the skeleton and then pruning the unwanted branches, the present study proposes a novel convolutional neural network based method to perform this task. The proposed architecture is an encoder-decoder network that leverages the benefits of the coordinated convolutional layer and multi-level supervision to prevent the loss of information between the extracted skeleton and the ground truth. The dense attention block is used as the backbone block in the encoder and decoder block. This architecture is performing better than the state of art on not only skeletonization of image tasks but also skeletonization from the point cloud. This method achieved an F1 score of 0.7961 on the Pixel Skeleton dataset and a Chamfer Distance (CD) score of 1.9561 on the Point skeleton dataset.