The goal of sign language technologies is to develop a bridging solution for the communication gap between the hearing-impaired community and the rest of society. Real-time Sign Language Recognition (SLR) is a state-of-the-art subject that promises to facilitate communication between the hearing-impaired community and others. Our research uses transfer learning to provide vision-based sign language recognition. We investigated recent works that use CNN-based methods and provided a literature review on deep learning systems for the sign language recognition (SLR) problem. This paper discusses the architecture of deep learning methods for SLR systems and explains a transfer learning application for fingerspelling sign classification. For the experiments, we used the Azerbaijani Sign Language Fingerspelling dataset and got 88.0% accuracy.