A common practice for addressing the problem of verifying the presence, or the consent of a person in many transactions is to utilize the handwritten signature. Among others, the offline or static signature is a valuable tool in forensic related studies. Thus, the importance of verifying static handwritten signatures still poses a challenging task. Throughout the literature, gray-level images, composed of handwritten signature traces are subjected to numerous processing stages; their outcome is the mapping of any input signature image in a so-called corresponding feature space. Pattern recognition techniques utilize this feature space, usually as a binary verification problem. In this work, sparse dictionary learning and coding are for the first time employed as a means to provide a feature space for offline signature verification, which intuitively adapts to a small set of randomly selected genuine reference samples, thus making it attractable for forensic cases. In this context, the K-SVD dictionary learning algorithm is employed in order to create a writer oriented lexicon. For any signature sample, sparse representation with the use of the writer's lexicon and the Orthogonal Matching Pursuit algorithm generates a weight matrix; features are then extracted by applying simple average pooling to the generated sparse codes. The performance of the proposed scheme is demonstrated using the popular CEDAR, MCYT75 and GPDS300 signature datasets, delivering state of the art results.