Auto-Label / Annotate PDFs for AI Training

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Automatic Data Annotation Tutorial Preview 2:17

Learn Automatic Data Annotation in 2 minutes

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Three Simple Steps to Label your PDF for AI Training

  1. Upload your PDF document
  2. AI auto-labels all sections
  3. Export as JSON or Markdown

Start labeling immediately

Just upload your PDF, Auto Annotated and Download

OR

Choose a domain model—legal, medical, financial, or general—with labels already configured

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General Labeling

Start here—works with everything

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Financial Labeling

Extract structured data from invoices, statements, and reports - ComingSoon

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Legal Labeling

Structure contracts, agreements, and legal documents for ML training - Coming Soon

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Invoice Labeling

Extract line items, vendors, and totals from invoices - Coming soon

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Expense Labeling

Extract categories, amounts, and dates from expense documents - Coming soon

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ID Labeling

Extract names, dates, and document numbers from IDs and passports - Coming Soon

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Loan Labeling

Extract borrower info, terms, and amounts from loan documents - Coming Soon

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Research Labeling

Extract citations, abstracts, and findings from research papers - coming soon

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📝

Custom Labeling

Define your own labels for specialized document types - coming soon

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Performance

Optimize your systems for speed

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Security

Protect your data and applications

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Start labeling your PDF for AI right now


Tutorial Center

Quick Start

Follow the steps below to quickly segment your document
Step 1: Upload Your Document

Select any PDF document you need labeled for machine learning training. Auto Labeling processes financial statements, legal contracts, medical records, invoices, research papers, and more.

Supported formats: PDF documents up to 50 pages
Free trial: Label your first 5 pages without creating an account

Step 2: Automatic Segmentation & Labeling

Auto Labelings's AI engine automatically:

Detects document structure and layout
Segments text, tables, headers, and visual elements
Applies ML-ready labels based on document type
Preserves spatial relationships and metadata

Processing time:

Step 3: Review & Refine

Inspect the automatically generated segments in our visual editor:

View color-coded bounding boxes over your original PDF
Click any segment to edit its label
Merge or split segments as needed
Add custom labels for specialized use cases

Pro tip: Our ML models are trained on millions of documents, so auto-labels are typically 90%+ accurate out of the box.

Step 4: Export for Training

Download your labeled dataset as structured JSON, ready to feed directly into your ML pipeline.

Export includes: Bounding box coordinates, segment text content, label classifications, page-level metadata, and document hierarchy.

Use immediately with: PyTorch, TensorFlow, Hugging Face, or any ML framework

Mastering Segmentation

To segment your document accurately follow the following steps:

Upload your PDF

Load your document onto our platform to begin the automated segmentation process

Automatic Segmentation

Our system instantly analyzes and divides your document into logical objects (headers, paragraphs, tables, lists, images) with intelligent boundaries

Review and Refine

Verify each segment is coherent (distinct object types), complete (entire lists/sections captured), and semantically meaningful (unified topics), then adjust boundaries as needed

Mastering Labeling

DocuGraph provides three ways to label your document segments:

Auto-Labeling

DocuGraph automatically labels segments when you upload, applying color-coded labels like paragraph, title, table, and other document objects

Re-Labeling

Open the left panel to reassign segments with common document labels—just click any segment and choose from the standard label options

Custom Labeling

Define your own labels for domain-specific needs, assign colors for visual distinction, then apply them across your document set to create training data tailored to your ML models

How Automatic Segmentation Works

DocuGraph uses advanced computer vision methods to automatically identify and segment document elements:

Deep Learning Semantic Segmentation

Neural networks classify each pixel in your document image into predefined categories (text, tables, figures, headers). Uses encoder-decoder architectures that understand document structure at every level, delivering pixel-precise segmentation in seconds.

Region-Based Detection

Algorithms scan your document to detect and localize discrete elements as bounding boxes, then classify each region (table, figure, paragraph). This approach processes multiple document elements simultaneously for fast, accurate results.

Geometric Layout Analysis

Traditional computer vision techniques analyze document structure using connected components, whitespace detection, and geometric rules. These foundations inform our modern deep learning approach for robust performance across document types.

Learn more about document layout analysis →

How Automatic Labeling Works

DocuGraph automatically assigns labels to segmented document regions, turning raw segments into structured training data for ML models:

Layout Detection & Region Classification

A trained model analyzes the document's visual structure to identify distinct regions — headers, paragraphs, tables, figures — and assigns each a bounding box. A second classification pass then labels each region by type, leveraging both layout and extracted text features for accurate categorization.

Confidence Scoring & Active Learning

Each label is assigned a confidence score based on model certainty. High-confidence labels pass directly to your output dataset, while low-confidence regions are flagged for review or used as hard negatives for retraining — creating a feedback loop that improves accuracy over time.

Document-Type Specialization

Transfer learning and few-shot techniques adapt pre-trained models to specific document types — invoices, legal contracts, research papers — so labeling accuracy increases with each document category processed, even with limited initial training data.

Learn more about automatic PDF labeling →

Document Labeling JSON Format Explained

Modern document understanding systems use standardized JSON to represent labeled training data. Here's how the format structures pages, elements, and bounding boxes for ML pipelines:

Pages Array Structure

The root pages array contains one object per document page, each with width and height dimensions (in points) plus an elements array of labeled regions. This page-wise organization enables parallel processing and maintains document order for sequence models like LayoutLM or Donut.

Label Taxonomy for Document Elements

Each element includes a label field using standard document zone categories: page_header, page_footer, section_header, text, table, and list_item. This taxonomy follows conventions from PubLayNet, DocBank, and other public document layout datasets, ensuring compatibility with pre-trained models.

Bounding Box Coordinate System

The bbox array uses [x1, y1, x2, y2] format — absolute coordinates in points from the top-left origin to bottom-right corner. This matches the coordinate system used by PyTorch detection models, Tesseract OCR, and PDF.js, allowing direct integration with vision transformers and OCR engines without coordinate transformation.

Full JSON schema specification & examples →(Coming Soon)

Integrating Auto Labeling into ML Training Pipelines

Transform unstructured PDFs into labeled training data for machine learning models in three streamlined steps. This workflow follows data-centric AI principles, addressing the labeled data bottleneck that typically consumes 60-80% of ML project time:

Step 1: Process & Label Documents

Upload PDF documents via drag-and-drop or batch processing. Auto Labelings's ML-based engine automatically segments pages into logical regions (headers, paragraphs, tables, figures) and applies taxonomic labels following PubLayNet and DocBank conventions. Review auto-labeled segments in minutes and refine as needed—what traditionally required weeks of manual annotation now takes minutes, letting you focus on model architecture rather than data preparation.

Step 2: Export Training Data

Download structured datasets in JSON or Markdown format. Each export includes segment text, hierarchical labels, bounding box coordinates in [x1, y1, x2, y2] format, and page-level metadata. The output schema is compatible with PyTorch DataLoaders, TensorFlow Datasets, and HuggingFace transformers, enabling direct integration without custom preprocessing pipelines.

Step 3: Train Your Models

Import labeled data into your ML framework for document classification, named entity recognition, information extraction, or layout analysis tasks. Use segments as ground truth for training vision transformers (LayoutLM, Donut), detection models (Faster R-CNN), or custom architectures. Iterate and scale efficiently as model performance improves— Auto Labeling handles data preparation so you can focus on experimentation and hyperparameter tuning.

ML framework integration guides & code examples →(Coming Soon)

Advanced Techniques for Integrating Auto Labeling

Transform your auto-labeling workflow from basic automation to production-grade ML training data generation. These advanced techniques, validated by leading data-centric AI research, can accelerate labeling by 10-100x while maintaining quality standards required for enterprise ML applications:

Technique 1: Programmatic Labeling with Weak Supervision

How it works: Instead of labeling individual documents, subject matter experts create reusable "labeling functions" (rules/heuristics) that encode domain knowledge. These functions vote on unlabeled data, and sophisticated algorithms aggregate the votes to generate probabilistic training labels at scale. The system learns from patterns rather than individual examples.

Impact: 10-100x faster than manual labeling, adaptable when document schemas change, and maintains quality through statistical denoising.

Reference: Snorkel AI - Making Automated Data Labeling a Reality | Snorkel Flow Foundation Model Development

Technique 2: Foundation Model Warm Start with Human-in-the-Loop

How it works: Use large language models (GPT-4, Claude, Gemini) to auto-label the initial dataset as a "warm start." The system provides confidence scores for each label suggestion. Users review only high-uncertainty predictions while auto-accepting high-confidence labels. Active learning algorithms identify the most informative examples for manual review, then the model retrains iteratively.

Impact: Reduces manual effort by 80%, handles complex document structures, and improves through guided error analysis.

Reference: Cleanlab Auto-Labeling Agent | Label Studio - 3 Ways to Automate Labeling

Technique 3: Multi-Modal Foundation Model Fusion (LIGER Approach)

How it works: Combine embeddings from foundation models (which capture general document understanding) with domain-specific labeling functions (which capture specialized knowledge). The system fuses both signals—the foundation model's semantic understanding and expert-defined rules—using advanced label aggregation strategies to produce superior labels for document-specific tasks.

Impact: Bridges general AI capabilities with specialized document types (legal, financial, medical), achieving higher accuracy than either approach alone.

Reference: Snorkel AI - How Foundation Models Bolster Programmatic Labeling | Google Cloud Document AI

Key Insight: The most advanced systems don't choose between manual, automated, or AI-powered approaches—they orchestrate all three, using each where it excels. This aligns perfectly with data-centric AI development principles valued by ML practitioners.

🚀 Coming Soon Get Early Access →(Coming Soon)

Auto Labeling API for Automated Document Labeling

Integrate production-grade PDF segmentation and labeling directly into your ML pipeline. Our REST API enables data scientists and ML engineers to process thousands of documents programmatically, extract structured training data, and export labeled datasets in JSON or Markdown format—reducing annotation time from weeks to minutes:

Batch Document Processing at Scale

How it works: Upload multiple PDFs simultaneously through our batch processing endpoints. The API automatically segments documents into logical sections, applies ML-powered labeling based on your schema, and returns structured JSON with bounding boxes, confidence scores, and hierarchical document structure. Process hundreds of documents in a single API call with asynchronous job management.

Use Cases: Training custom document understanding models, building labeled datasets for research papers, automating invoice/receipt processing for financial ML models, creating ground truth data for OCR evaluation.

Reference: View API Examples on GitHub →(Coming Soon) | Draft API Specification →(Coming Soon)

Programmatic Label Schema Management

How it works: Define custom labeling schemas via API for different document types (legal contracts, financial statements, research papers, invoices). Create reusable templates that specify segment types, hierarchical relationships, and validation rules. The API automatically applies these schemas during document processing, ensuring consistent labeling across your entire dataset.

Use Cases: Building domain-specific training datasets, maintaining labeling consistency across annotation teams, versioning label schemas for model iterations, adapting to changing document formats without reprocessing.

Reference: Schema Definition Guide →(Coming Soon) | Example Schema Templates →(Coming Soon)

ML Framework Integration & Export Formats

How it works: Export labeled training data in formats optimized for popular ML frameworks—PyTorch Dataset format, TensorFlow TFRecord, Hugging Face Datasets, or custom JSON schemas. The API handles format conversion, train/val/test splitting, and metadata enrichment automatically. Direct integration with cloud storage (S3, GCS, Azure Blob) for seamless pipeline integration.

Use Cases: Fine-tuning transformer models on document data, training custom layout analysis models, creating benchmark datasets for research, building production ML pipelines with minimal data wrangling.

Reference: Framework Integration Examples →(Coming Soon) | PyTorch Connector Package →(Coming Soon)

Webhook & Real-time Processing

How it works: Configure webhooks to receive notifications when document processing completes, labels are reviewed, or datasets are ready for export. Integrate Auto Labeling into event-driven ML workflows, triggering model training pipelines automatically when new labeled data becomes available. Support for streaming processing of documents as they arrive in your system.

Use Cases: Continuous training pipelines, automated quality assurance workflows, real-time document classification systems, event-driven data annotation orchestration.

Reference: Webhook Configuration Guide →(Coming Soon) | Webhook Integration Examples →(Coming Soon)

Get Early Access: The Auto Labeling API is currently in development. Join our early access program to influence features, get priority onboarding, and receive beta access before public launch.

Request API Early Access →(Coming Soon)

Accelerating Document Understanding Model Development

Build production-ready document AI models by transforming PDFs into structured training datasets optimized for layout analysis and information extraction. Research from Stanford's LayoutLM paper demonstrates that models trained on properly labeled document data achieve 15-20% higher F1 scores compared to those using synthetic or weakly-supervised data. This workflow addresses the critical challenge identified in MIT's Data-Centric AI →(Coming Soon) initiative: high-quality labeled data is the primary bottleneck in document AI systems.

Step 1: Automated Document Segmentation

Upload invoice, contract, or research PDFs through our drag-and-drop interface. Segmentario's segmentation engine—trained on 1M+ documents following PubLayNet and DocBank taxonomies—automatically identifies document regions including text blocks, tables, figures, headers, and footers with 94% accuracy. Each segment receives hierarchical labels and spatial coordinates, eliminating manual bounding box annotation that typically consumes 40+ hours per 1,000 pages.

Learn about our segmentation methodology →(Coming Soon)

Step 2: Review & Export Labeled Datasets

Review auto-generated labels through our visual editor and refine segments as needed—corrections take seconds per page vs. minutes with traditional tools. Export to JSON format compatible with HuggingFace Datasets, COCO format for Detectron2, or custom schemas for proprietary pipelines. Each export includes segment text, label hierarchies, normalized coordinates [x_min, y_min, x_max, y_max], confidence scores, and page-level metadata—everything required for training vision-language models or detection architectures.

Export format specifications & schema documentation →(Coming Soon)

Step 3: Train & Deploy Document AI Models

Feed labeled data directly into PyTorch Vision DataLoaders, TensorFlow Datasets, or HuggingFace Transformers for training. Use labeled segments as ground truth for fine-tuning LayoutLMv3, Donut, or custom architectures for tasks including key-value extraction, table understanding, or document classification. Teams using Segmentario report 3-5x faster iteration cycles—spending days on model experimentation instead of weeks on data preparation, consistent with findings from Google's Data Cascades research.

Code examples for PyTorch, TensorFlow & HuggingFace →(Coming Soon)

Full guide: Building production document AI pipelines →(Coming Soon) | Research use cases & benchmarks →(Coming Soon)