The use of a person's signature is considered as one of the most commonly used biometric methods for recognition, either as a standalone feature or as part of multimodal systems. Based on the inter-writer variability, handwritten signatures have been accepted as a personal trait in many transactions to verify the consent or the author's presence. The main challenges for handwritten signature verification are the limited amount of available data for each writer and the intra-writer variability. Sparse Representation (i.e. dictionary learning and sparse coding) achieved state of the art results at handwritten signature verification, using only a small set of genuine reference samples of the writer. The extension of Sparse Representation to an efficient multi-layer architecture is Deep Sparse Coding. Deep Sparse Coding architecture connects multiple layers of Sparse Representation with a sparse-to-dense module in order to enable Sparse Coding on deeper layers. Learning sparse representations at different levels of abstraction leads to building feature hierarchies. The sparse-to-dense module involves a Dimensionality Reduction process, which was implemented with simple and fast methods, such as Random Orthogonal projection or PCA; instead of the computational expensive DrLIM. The performance of the proposed Deep Sparse Coding architecture to the challenging problem of offline signature verification is demonstrated in the popular CEDAR signature dataset, delivering improved state of the art results.