  With the rapid growth of Large Language Models (LLMs), safeguarding textual
content against unauthorized use is crucial. Text watermarking offers a vital
solution, protecting both - LLM-generated and plain text sources. This paper
presents a unified overview of different perspectives behind designing
watermarking techniques, through a comprehensive survey of the research
literature. Our work has two key advantages, (1) we analyze research based on
the specific intentions behind different watermarking techniques, evaluation
datasets used, watermarking addition, and removal methods to construct a
cohesive taxonomy. (2) We highlight the gaps and open challenges in text
watermarking to promote research in protecting text authorship. This extensive
coverage and detailed analysis sets our work apart, offering valuable insights
into the evolving landscape of text watermarking in language models.
