Section Boundary Detection

A sequence labeling approach where each token is classified as the Beginning, Inside, or Outside of a structural element using the BIO tagging scheme. Transformer-based models are fine-tuned on annotated legal corpora to predict whether a token marks a section start or end. This method excels at handling ambiguous formatting by learning contextual patterns rather than relying on explicit markup.

• Input: Tokenized text stream from OCR or native digital documents
• Output: Labeled spans corresponding to articles, recitals, and schedules
• Advantage: Robust to inconsistent numbering and formatting conventions

A rule-based method that infers document hierarchy by analyzing typographic features including font size, weight, style, and indentation. Changes in these properties signal transitions between structural levels. This approach is particularly effective for born-digital PDFs where font metadata is preserved and consistent.

• Detects headings by identifying font size jumps above body text baseline
• Uses bold and italic flags to distinguish section titles from emphasis
• Reconstructs nesting via left-margin indentation analysis
• Limitation: Fails on scanned documents without reliable OCR font reconstruction

Represents text blocks as nodes and their spatial or semantic relationships as edges in a graph structure. Algorithms such as minimum spanning trees or graph neural networks then infer the correct reading order and hierarchical organization. This technique handles complex multi-column layouts, footnotes, and inset elements that confuse linear parsers.

• Spatial edges: Proximity and alignment between text blocks
• Semantic edges: Content similarity and cross-reference links
• Output: A directed acyclic graph representing the document's logical structure

The systematic reconstruction of a document's outline tree by identifying section titles and subtitles and establishing their parent-child relationships. This process combines multiple signals including numbering patterns, typography, and semantic content to build a nested table of contents.

• Parses multi-level numbering schemes including Roman numerals, Arabic digits, and alphanumeric sequences
• Handles Romanet parsing for traditional lowercase Roman numeral outlines
• Resolves numbering gaps and inconsistencies common in amended legislation
• Produces a structured JSON or XML representation of the document skeleton

The targeted isolation of binding, actionable clauses from prefatory recitals and boilerplate language. This distinction is critical because operative provisions carry legal force while recitals merely provide context. Machine learning classifiers are trained to recognize the linguistic and structural markers that signal this transition.

• Identifies performative language such as 'shall', 'agrees to', and 'warrants'
• Detects the recital-to-operative boundary marked by 'Now, therefore' or similar transitional phrases
• Separates definition sections, representations, covenants, and conditions
• Essential for downstream obligation extraction and compliance checking

The process of reconstructing logical document structure from the unstructured stream of drawing commands in a PDF file. Unlike native digital documents, PDFs often discard semantic information, storing only visual positioning instructions. Structural extraction reverses this loss by inferring paragraphs, headings, and lists from spatial coordinates.

• Groups text fragments into words, lines, and paragraphs based on proximity
• Detects reading order across columns and floating elements
• Identifies tables by analyzing alignment patterns and ruling lines
• Handles Bates numbering and other marginalia as special zones