The Amount of legal information that is being produced on a daily basis in the law courts is increasing enormously and nowadays this information is available in electronic form also. The application of various machine learning and deep learning methods for processing of legal documents has been receiving considerate attention over the last few years. Legal document classification, translation, summarization, contract review, case prediction and information retrieval are some of the tasks that have received concentrated efforts from the research community. In this survey, we have performed a comprehensive study of various deep learning methods applied in the legal domain and classified various legal tasks into three broad categories, viz. legal data search, legal text analytics and legal intelligent interfaces. The proposed study suggests that deep learning models like CNNs, RNNs, LSTM and GRU, and multi-task deep learning models are being used actively to solve wide variety of legal tasks and are giving state-of-the-art performance.