Communication is an essential part of day-to-day life. However, people who have disabling hearing loss, which in general is known as deafness, or have a severe loss of hearing use sign language for their communication. Those people learn this language through their respective schools, whereas hearing people rarely get to know this communication tool, leaving a wide communication gap. To bridge this gap, an automated communication system that can convert text to sign language without depending on human interpreters. This literature review overviews sign language and discusses the different technologies used for text-to-sign conversion systems. The paper evaluates computer-based text processing methods, machine translation techniques (rule-based, corpus-based, and neural architectures), and sign synthesis approaches (avatar animation, video concatenation, neural generation). The paper compares each method's strengths and limitations and presents quantitative performance data from recent systems. The review also provides a comprehensive summary of widely used datasets and introduces a visual taxonomy of translation approaches. Furthermore, the paper discusses the current limitations, including scalability, data limitations, the need for more expressive synthesis, and future scope for improvement. The expected outcome of this review is to find and gather information on various sign language systems worldwide, identify research gaps, and propose further enhancements in text-to-sign language conversion.