This paper provides a comprehensive survey of deep learning techniques for Named Entity Recognition (NER). NER is the task of identifying mentions of rigid designators in text belonging to predefined semantic types such as person, location, organization, etc. NER serves as a foundation for many natural language processing applications. Early NER systems relied on human-engineered features and rules, but recent advances in deep learning have significantly improved performance. This survey reviews existing deep learning techniques for NER, categorizes them along three axes: distributed representations for input, context encoder, and tag decoder. It also surveys recent deep learning methods in new NER problem settings and applications. The paper highlights challenges faced by NER systems and outlines future directions in this area. It also provides a taxonomy of DL-based NER approaches, including distributed representations for input, context encoder, and tag decoder. The survey discusses various deep learning techniques for NER, including distributed representations for input, context encoder architectures, and tag decoder architectures. It also reviews traditional approaches to NER, including rule-based, unsupervised learning, and feature-based supervised learning approaches. The paper concludes with a discussion of the challenges and future directions in NER research.This paper provides a comprehensive survey of deep learning techniques for Named Entity Recognition (NER). NER is the task of identifying mentions of rigid designators in text belonging to predefined semantic types such as person, location, organization, etc. NER serves as a foundation for many natural language processing applications. Early NER systems relied on human-engineered features and rules, but recent advances in deep learning have significantly improved performance. This survey reviews existing deep learning techniques for NER, categorizes them along three axes: distributed representations for input, context encoder, and tag decoder. It also surveys recent deep learning methods in new NER problem settings and applications. The paper highlights challenges faced by NER systems and outlines future directions in this area. It also provides a taxonomy of DL-based NER approaches, including distributed representations for input, context encoder, and tag decoder. The survey discusses various deep learning techniques for NER, including distributed representations for input, context encoder architectures, and tag decoder architectures. It also reviews traditional approaches to NER, including rule-based, unsupervised learning, and feature-based supervised learning approaches. The paper concludes with a discussion of the challenges and future directions in NER research.