Document Understanding

Kuzushiji was a widely used cursive writing system in pre-modern Japan. Due to simplification and glyph variation, most modern Japanese readers cannot read Kuzushiji characters. Consequently, recent studies have developed optical character recognition (OCR) systems for Kuzushiji. Despite recent progress, Kuzushiji character recognition (KCR) in Japanese historical documents remains challenging because of seal-character overlap and complex layouts, which interfere with character recognition and hinder accurate reconstruction of the reading order. To address these challenges, we propose a multi-stage KCR framework comprising character detection, cropping, classification, ordering, and large language model (LLM)-based post-OCR correction. Specifically, we employ a synthetic data augmentation strategy to improve character detection robustness against seal interference and introduce an adaptive column clustering algorithm to reconstruct the reading order. Finally, we leverage the contextual capabilities of the LLM to correct OCR errors. In addition, we correct annotation omissions, reconstruct the benchmark dataset, and introduce a synthetic test set with simulated seal interference and an out-of-domain (OOD) test set for evaluation. Compared with the conventional character-level OCR baseline, our framework achieves relative CER reductions of 43.48%, 46.02%, and 39.11% on the real, synthetic, and OOD test sets, respectively.

Manufacturing automation in process planning, inspection planning, and digital-thread integration depends on a unified specification that binds the geometric features of a 3D CAD model to the geometric dimensioning and tolerancing (GD&T) callouts, datum definitions, and surface requirements carried by the corresponding 2D engineering drawing. Although Model-Based Definition (MBD) allows such specifications to be embedded directly in 3D models, 2D drawings remain the primary carrier of manufacturing intent in automotive, aerospace, shipbuilding, and heavy-machinery industries. Correctly linking drawing annotations to the corresponding 3D features is difficult because of contextual ambiguity, repeated feature patterns, and the need for transparent and traceable decisions. This paper presents a deterministic-first, context-aware framework that maps 2D drawing entities to 3D CAD features to produce a unified manufacturing specification. Drawing callouts are first semantically enriched and then scored against candidate features using an interpretable metric that combines type compatibility, tolerance-aware dimensional agreement, and conservative context consistency, along with engineering-domain heuristics. When deterministic scoring cannot resolve an ambiguity, the system escalates to multimodal and constrained large-language-model reasoning, followed by a single human-in-the-loop (HITL) review step. Experiments on 20 real CAD-drawing pairs achieve a mean precision of 83.67%, recall of 90.46%, and F1 score of 86.29%. An ablation study shows that each pipeline component contributes to overall accuracy, with the full system outperforming all reduced variants. By prioritizing deterministic rules, clear decision tracking, and retaining unresolved cases for human review, the framework provides a practical foundation for downstream manufacturing automation in real-world industrial environments.

Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges for manual sorting and analysis. These archives contain diverse content, including various text types (handwritten, typed, printed), graphical elements (drawings, maps, photos), and layouts (plain text, tables, forms). Efficiently processing this heterogeneous data requires automated methods to categorize pages based on their content, enabling tailored downstream analysis pipelines. This project addresses this need by developing and evaluating an image classification system specifically designed for historical document pages, leveraging advancements in artificial intelligence and machine learning. The set of categories was chosen to facilitate content-specific processing workflows, separating pages requiring different analysis techniques (e.g., OCR for text, image analysis for graphics)