Computational analyses of texts are often based on prior quantifications of low-level linguistic features, such as the most frequent words or occurrences of specific grammatical constructions. Arguably, analyses on the basis of such data are intellectually remote from traditional forms of literary scholarship, which generally focuses on the description and the interpretation of aspects such as meter, figures of speech, imagery or themes. In this presentation, the results are presented of a study which has made a contribution to the alignment of traditional practices and scholarship based on data processing, through the quantification of a wide range of literary devices. The focus in the study was on the investigation of poetry. Software was built for the recognition of various forms of rhyme, alliteration, enjambment, onomatopoeia, refrains and forms of imagery. The resultant annotations have been recorded on the basis of the data model that was proposed by the Open Annotation Collaboration. In addition, a number of techniques have been developed for the visualisation of these annotations. These visualisation techniques can firstly be used to expose patters within the corpus in its entirety, allowing for a form of distant reading. Next to this, the graphic abstractions derived from data on individual poems may also support close reading processes. The algorithms gave been tested extensively on machine-readable versions of the poetry of Louis MacNeice. The software that was developed enables scholars to explore correlations between, for instance, specific figures of speech and imagery, or to identify noteworthy uses of literary devices which specific parts of the corpus. Such forms of analysis clearly help to bridge a gap between the essentially quantitative and realist inclinations of the toolset on the one hand, and the largely interpretative and qualitative approach of the discipline in which these methods are adopted on the other.