In many public and private institutions, the digitalization of handwritten documents has progressed greatly in recent decades. As a consequence, the number of handwritten documents that are available digitally is constantly increasing. However, accessibility to these documents in terms of browsing and searching is still an issue as automatic full transcriptions are often not feasible. To bridge this gap, Keyword Spotting (KWS) has been proposed as a flexible and error-tolerant alternative to full transcriptions. KWS provides unconstrained retrievals of keywords in handwritten documents that are acquired either online or offline. In general, offline KWS is regarded as the more difficult task when compared to online KWS where temporal information on the writing process is also available. The focus of this chapter is on handwritten historical documents and thus on offline KWS. In particular, we review and compare different state-of-the-art as well as novel approaches for template-based KWS. In contrast to learning-based KWS, template-based KWS can be applied to documents without any a priori learning of a model and is thus regarded as the more flexible approach.