Car license plate detection has been always a challenging task in the context of traffic control and traffic offenses. In this paper, the problem of license plate detection from gray scale images taken in natural conditions is addressed. Our car plate database consists of images with severe imaging conditions such as low quality images, far distanced cameras, and severe weather conditions. The main proposed idea in this paper, is to learn discriminative dictionaries from plate and non-plate exemplars for the classification task. A well-known dictionary learning algorithm is employed for this purpose. However, initial preprocessing techniques for image enhancement are also applied. The achieved results reveal that sparse representation-based techniques in the proposed method can increase the detection accuracy and improve the previously reported results.