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Visual Element Extraction and Grouping from Eighteenth-Century Books based on Deep-Learning Methods

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Title: Visual Element Extraction and Grouping from Eighteenth-Century Books based on Deep-Learning Methods
Author(s): Wang, Ruilin
Contributor: University of Helsinki, Faculty of Science
Degree program: Master's Programme in Computer Science
Specialisation: Algorithms
Language: English
Acceptance year: 2023
This thesis is an integral part of an interdisciplinary research endeavor that provides computer science-driven approaches and deep learning methodologies that seamlessly integrate into the broader research conducted by scholars in the field of digital humanities and historians. Utilizing deep learning techniques, this research investigates the printers and publishers of 18th-century books, with a specific focus on the prominent family-based printing dynasty called Tonson. By identifying common visual elements, the thesis facilitates a comparative analysis of associations between different printers, providing valuable insights into the historical context and artistic characteristics of the Tonson dynasty. The thesis begins by discussing various deep learning models trained on an expert-annotated dataset, enabling the extraction of five main categories and sixteen subcategories of visual elements. Notably, the MaskRCNN model demonstrates superior performance, particularly in detecting headpieces and initials. The study then delves into the grouping of initials and headpieces within the dataset. The Sim CLR model is employed using data augmentation techniques that simulate the inherent noise present in the dataset. This enables the generation of distinct embeddings for each initial and headpiece. Various unsupervised learning methods are applied, with Hierarchical Clustering emerging as the most effective technique. Higher similarity scores for headpieces compared to initials indicate greater ease in identifying similar headpieces. We further discuss the potential applications from a historical perspective including book pricing, future avenues for accurately identifying related printers, and temporal research concerning the Tonson dynasty. In conclusion, this thesis presents a novel integration of computer science and deep learning methodologies within the field of digital humanities and historical studies. By focusing on the Tonson dynasty, it provides a comprehensive analysis of printers and publishers in 18th-century books, ultimately contributing to a deeper understanding of this historical period.
Keyword(s): object detection historical book analysis self-supervised representation learning

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