Clustering Running Titles to Understand the Printing of Early Modern Books

TL;DR

This paper introduces a computational method using neural and feature-based clustering to analyze and detect deviations in running titles of early modern books, aiding bibliographic and historical research.

Contribution

It presents a novel approach combining visual similarity kernels and clustering to analyze printing patterns in early modern books, revealing deviations and transmission phenomena.

Findings

Successfully clusters running titles across multiple books

Detects deviations indicating printing irregularities or censorship

Validates method against manual bibliographic analysis

Abstract

We propose a novel computational approach to automatically analyze the physical process behind printing of early modern letterpress books via clustering the running titles found at the top of their pages. Specifically, we design and compare custom neural and feature-based kernels for computing pairwise visual similarity of a scanned document's running titles and cluster the titles in order to track any deviations from the expected pattern of a book's printing. Unlike body text which must be reset for every page, the running titles are one of the static type elements in a skeleton forme i.e. the frame used to print each side of a sheet of paper, and were often re-used during a book's printing. To evaluate the effectiveness of our approach, we manually annotate the running title clusters on about 1600 pages across 8 early modern books of varying size and formats. Our method can detect potential deviation from the expected patterns of such skeleton formes, which helps bibliographers understand the phenomena associated with a text's transmission, such as censorship. We also validate our results against a manual bibliographic analysis of a counterfeit early edition of Thomas Hobbes' Leviathan (1651).