Templify SDK: A Multi-Axis Approach to Classifying Document Lines

rodneymbrown1
Sep 12
3 min read

If you’ve ever tried to process documents like contracts, resumes, or research papers, you know the pain: they look structured to humans but appear as a messy stream of text to a computer. A human sees “1. INTRODUCTION” and instantly knows it’s a heading. A parser might treat it like any other sentence.

That’s where the Templify SDK comes in. Instead of brittle regex rules or heavy machine learning, Templify introduces a multi-axis taxonomy for classifying lines of text. This approach gives you a reliable, extensible way to understand the structure of domain-specific documents and use that structure for automation, templating, and LLM workflows.

Why Line Classification Matters

Think about common document types:

Legal filings → clauses, numbered sections, citations
Resumes → section headers, bullets, body summaries
Study guides → key terms, definitions, outline hierarchies
Research papers → abstracts, headings, references

All of these rely on structure. Without it, you can’t:

Insert new content into the right place
Map sections to a template
Feed clean, structured input to an LLM

The trick is classifying each line correctly — not just by what it says, but by how it functions in the document.

The Multi-Axis Taxonomy

Templify looks at every line of text through four independent lenses:

Structural Form
- What does the line look like?
- Examples: ALLCAPS, short title phrases, numbered items, bullets.
Heuristic Signals
- What features or patterns are present?
- Regex matches, capitalization ratio, indentation, spacing, punctuation markers.
Semantic Role
- What is the line trying to do?
- Section heading, instruction, disclaimer, citation, body text.
Contextual Position
- Where is it in relation to other lines?
- Does it follow a heading? Precede a list? Mark the start of a new section?

By combining these axes, Templify produces a pattern class.For example:

Line: "1. INTRODUCTION" Classification: H-SHORT + NumericPrefix + HeadingRole + SectionStart

A regex parser might fail here (is it a number? is it a title?).Templify succeeds because the combination of clues makes the intent unambiguous.

Implementation in the SDK

The SDK provides:

Feature extractors → functions to capture line-level features (caps ratio, bullets, numbering, etc.)
Heuristic classifier → scores lines against feature weights
Pattern modules → reusable classes for common structures (headings, bullets, warnings, citations)
Extensibility → add your own heuristics or semantic roles without retraining a model

It’s designed to be modular and transparent. You can inspect why a line was classified a certain way and extend the system as your domain demands.

Domain Examples

Resumes:
- Distinguish section headers (“Education”) from bullets (“• Built CI/CD pipeline”).
Legal filings:
- Detect numbered clauses and citations that look similar to body text.
Study guides:
- Separate bolded key terms from definitions and outline markers.
Research papers:
- Identify abstracts, references, and heading hierarchies.

Each of these domains has quirks that break regex rules. A multi-axis approach handles them gracefully.

Why Not Just Use Machine Learning?

Models like LayoutLM are powerful — they combine text and layout information to understand documents. But they’re also:

Data-hungry (require lots of annotated training data)
Compute-heavy (not lightweight for dev workflows)
Opaque (hard to debug why something was classified a certain way)

Templify takes a middle path:

Rule-based but flexible
Lightweight (no GPU required)
Transparent (you can see exactly which clues fired)

It’s not competing with LayoutLM — it’s giving developers a tool they can trust and extend without setting up a full ML pipeline.

The Takeaway

Templify’s multi-axis taxonomy makes line classification in domain-specific documents robust, extensible, and transparent. By treating each line as the intersection of structure, heuristics, semantics, and context, it bridges the gap between raw text and structured templates.

Why Line Classification Matters

Think about common document types:

Legal filings → clauses, numbered sections, citations

Resumes → section headers, bullets, body summaries

Study guides → key terms, definitions, outline hierarchies

Research papers → abstracts, headings, references

All of these rely on structure. Without it, you can’t:

Insert new content into the right place

Map sections to a template

Feed clean, structured input to an LLM

The trick is classifying each line correctly — not just by what it says, but by how it functions in the document.

The Multi-Axis Taxonomy

Templify looks at every line of text through four independent lenses:

Structural Form

What does the line look like?

Examples: ALLCAPS, short title phrases, numbered items, bullets.

Heuristic Signals

What features or patterns are present?

Regex matches, capitalization ratio, indentation, spacing, punctuation markers.

Semantic Role

What is the line trying to do?

Section heading, instruction, disclaimer, citation, body text.

Contextual Position

Where is it in relation to other lines?

Does it follow a heading? Precede a list? Mark the start of a new section?

By combining these axes, Templify produces a pattern class.For example:

Line: "1. INTRODUCTION" Classification: H-SHORT + NumericPrefix + HeadingRole + SectionStart

A regex parser might fail here (is it a number? is it a title?).Templify succeeds because the combination of clues makes the intent unambiguous.

Implementation in the SDK

The SDK provides:

Feature extractors → functions to capture line-level features (caps ratio, bullets, numbering, etc.)

Heuristic classifier → scores lines against feature weights

Pattern modules → reusable classes for common structures (headings, bullets, warnings, citations)

Extensibility → add your own heuristics or semantic roles without retraining a model

It’s designed to be modular and transparent. You can inspect why a line was classified a certain way and extend the system as your domain demands.

Domain Examples

Resumes:

Distinguish section headers (“Education”) from bullets (“• Built CI/CD pipeline”).

Legal filings:

Detect numbered clauses and citations that look similar to body text.

Study guides:

Separate bolded key terms from definitions and outline markers.

Research papers:

Identify abstracts, references, and heading hierarchies.

Each of these domains has quirks that break regex rules. A multi-axis approach handles them gracefully.

Why Not Just Use Machine Learning?

Models like LayoutLM are powerful — they combine text and layout information to understand documents. But they’re also:

Data-hungry (require lots of annotated training data)

Compute-heavy (not lightweight for dev workflows)

Opaque (hard to debug why something was classified a certain way)

Templify takes a middle path:

Rule-based but flexible

Lightweight (no GPU required)

Transparent (you can see exactly which clues fired)

It’s not competing with LayoutLM — it’s giving developers a tool they can trust and extend without setting up a full ML pipeline.

The Takeaway

Templify’s multi-axis taxonomy makes line classification in domain-specific documents robust, extensible, and transparent. By treating each line as the intersection of structure, heuristics, semantics, and context, it bridges the gap between raw text and structured templates.

That means fewer brittle regex rules, no dependence on opaque ML, and a cleaner pipeline for integrating with LLMs.

Whether you’re working with contracts, resumes, study guides, or research papers, Templify helps you capture the structure your automation depends on.

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