Digital Archives and AI for Textual Restoration in 19th-Century Novels

Thesis Abstract

The study addresses the persistent challenge of textual fragmentation and non-canon revisions in 19th-century novels, where archival gaps, editorial marks, and faded typography obscure authorial intention and historical reception. The problem is compounded by limited access to comprehensive digital corpora and the absence of scalable, transparent restoration workflows that can be audited by literary scholars. This research aims to develop a technology-driven workflow that synthesizes digital archives with AI-powered restoration to reconstruct authorial text, restore orthographic conventions, and annotate editorial interventions while preserving provenance. The objectives are to (1) assemble a multimodal corpus of 19th-century novels, including scanned archival pages, diplomatic transcriptions, and edition histories; (2) design an AI-driven restoration pipeline that combines OCR correction, language-model-based restoration, and alignment with known editorial practices; (3) evaluate restoration quality against baseline human-edited texts using objective and interpretive metrics; (4) examine the impact of restored texts on textual interpretation and historical sensibility through qualitative analysis; and (5) articulate a reproducible framework and tooling for scholarly use. The methodology adopts a mixed-methods design grounded in digital humanities and AI-assisted textual criticism. The population comprises major 19th-century novels with substantial archival fragmentation (e.g., Jane Austen, Charles Dickens, and George Eliot) and their corresponding editorial histories. A sample of 12 novels with at least three archival variants each will be selected, yielding approximately 36 archival fragments and 12 canonical editions for comparison. Data collection will involve (a) digitized archival pages sourced from national libraries and university archives, (b) existing scholarly editions and modern critical texts, and (c) metadata from provenance records. Instruments will include a custom restoration pipeline built upon state-of-the-art AI language models fine-tuned on 19th-century English, OCR post-correction modules, alignment algorithms, and provenance-tracking components. Validity and reliability will be established through cross-validation against expert-edited passages, inter-translator reliability checks for editorial annotations, and reproducibility tests using containerized workflows. Analytical approaches combine quantitative and qualitative techniques. The restoration quality will be assessed using precision/recall metrics for character recovery, orthographic normalization accuracy, and lexical-mean-squared-error relative to reference editions. A regression analysis will examine factors predicting restoration accuracy, including manuscript age, ink degradation level, and script variety. The alignment of restored texts with canonical editions will be evaluated using Cohen’s kappa and sequence alignment scores. Theoretical interpretation will employ Bayesian uncertainty quantification to model confidence in restored segments. Thematic analysis, guided by New Historicism and reader-response theory, will explore how restoration alters interpretive cues such as voice, sentiment, and stylistic markers. A conceptual model will depict interactions among archival evidence, model outputs, editorial practice, and scholarly interpretation. Key expected findings include demonstrable improvements in textual fidelity to authorial intention, measurable reductions in editorial ambiguity, and enhanced alignment between archival evidence and modern scholarly editions. The AI-driven pipeline is anticipated to outperform baseline OCR and rule-based restoration in terms of restoration accuracy and reproducibility. The study is expected to reveal nuanced shifts in interpretation when readers engage with restored versions, highlighting the role of orthography and punctuation in encoding social and stylistic cues. The contribution to knowledge lies in (a) providing a transparent, auditable restoration workflow that integrates archival provenance with AI-assisted reconstruction; (b) delivering empirically validated restoration techniques applicable to large-scale 19th-century corpora; and (c) offering methodological insights into how digital archives can transform literary criticism by restoring historical authorial practices. The main conclusion is that AI-enhanced textual restoration, grounded in robust archival provenance and complemented by qualitative analysis, can meaningfully improve scholarly access to 19th-century novels without sacrificing critical interpretive integrity. Recommendations include expanding the corpus to include multilingual editions, developing standardized provenance schemas for restoration outputs, and fostering interdisciplinary training for literary scholars in AI-assisted methods to ensure ethical handling of contested editorial histories.