Improved full-text search, named-entity recognition and relationship extraction are all key research topics across many areas of technology, with emerging applications in the intelligence, healthcare and financial fields amongst many others  . In Digital Humanities, there is a growing interest in the application of such Natural Language Processing (NLP) approaches to historical texts  with a view to improving how a user can explore and analyse these collections     . However, the text contained in handwritten historical manuscript collections can often be 'noisy' in nature — with variation in spelling, punctuation, word form, sentence structure and terminology. This is particularly the case with collections written in archaic language forms, such as Early-Modern English. Multiple studies have concluded that the applicability of modern NLP tooling to such historical texts has been very limited due to this inherent noisiness in the texts. This historical language barrier hinders the accessibility and thus the potential exploration and analysis of many significant historical text collections. This paper will discuss the normalisation of historical texts as a solution to this problem and examine how normalisation can improve the analysis, interpretation and exploration of these collections. Normalisation is the process of transforming text into a single canonical form, in this case, the modern equivalent of the language. Once this has been completed, the texts can be processed using current NLP techniques and technologies. However, the normalisation of historical texts presents a difficult challenge in itself. Much research has been undertaken in an attempt to cope with the correction and normalisation of text produced by Optical Character Recognition (OCR), speech recognition, instant messaging etc. which show similar characteristics to those of historical texts. One technique which has been applied is the use of a historical lexicon, supplemented by computational tools and linguistic models of variation. However, because of the absence of language standards, multiple orthographic variations of a given word or expression can be found in a collection of material, even in the same document. As a result, the quality of the results achieved, even after normalisation, has not been satisfactory. Researchers have also noted a general lack of tools and resources specialised to this domain. This paper will present the normalisation research conducted as part of the CULTURA project, which has developed techniques for the normalisation of a 17th century manuscript collection written in Early Modern English, The 1641 Depositions  . CULTURA analyses the artefacts and through the application of novel linguistic models of variation, enables normalisation techniques to remove issues of inconsistency in spelling, grammar and punctuation. The technologies developed and applied have had to solve issues arising from the need to contend with noisy inputs, the impact noise can have on downstream applications, and the demands that noisy information places on document analysis. The normalisation of texts in Early Modern English can be interpreted as a special (restricted) case of translation. Using this intuition, a methodology was developed based upon statistical machine translation models. The key ingredient of this approach is a new translation module that further develops known OCR correction techniques.