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TECHNICAL GUIDE

Multi-Modal AI for Document Intelligence

Understanding how vision-language models extract tables, charts, and figures from PDFs with unprecedented accuracy

πŸ“– 18 min readβ€’Technical Level: Advancedβ€’Last updated: November 2025

Table of Contents

→1. Introduction to Multi-Modal AI→2. The Document Intelligence Challenge→3. Vision-Language Model Architecture→4. How Multi-Modal AI "Sees" Documents→5. Table Extraction Deep Dive→6. Chart & Figure Analysis→7. Model Comparison & Selection→8. Quality Assurance & Validation→9. Performance Optimization→10. Best Practices & Limitations

Introduction to Multi-Modal AI

Multi-modal AI represents a fundamental shift in how machines process documents. Unlike traditional OCR or text-based extraction, multi-modal models can simultaneously process visual and textual information, much like humans do when reading documents.

What Makes It "Multi-Modal"?

Multi-modal refers to the model's ability to process multiple types of input simultaneously:

  • βœ“ Visual modality: Images, layouts, formatting, spatial relationships
  • βœ“ Textual modality: Words, sentences, semantic meaning
  • βœ“ Structural modality: Tables, hierarchies, document organization

Evolution of Document Processing

Generation 1: Traditional OCR (1980s-2010s)

Pattern matching and character recognition

  • β€’ Accuracy: 60-80% on complex documents
  • β€’ Struggles with: Tables, merged cells, complex layouts
  • β€’ Cannot understand context or meaning

Generation 2: Deep Learning OCR (2010s-2020)

Neural networks for text recognition

  • β€’ Accuracy: 85-95% on clean documents
  • β€’ Better at: Handwriting, varied fonts, rotated text
  • β€’ Still limited: Structure understanding, semantic context

Generation 3: Multi-Modal AI (2023+)

Vision-language models with contextual understanding

  • β€’ Accuracy: 95-99% on complex regulatory documents
  • β€’ Excels at: Tables, charts, figures, nested structures
  • β€’ Understands: Context, meaning, relationships, intent

The Document Intelligence Challenge

Regulatory documents present unique challenges that traditional extraction methods cannot handle effectively:

Why Regulatory PDFs Are Difficult

πŸ“ŠComplex Table Structures

  • β€’ Multi-level headers (3-4 levels deep)
  • β€’ Merged cells spanning rows and columns
  • β€’ Nested tables within cells
  • β€’ Rotated text in headers
  • β€’ Variable cell borders and shading

πŸ“ˆEmbedded Visualizations

  • β€’ Charts as raster images (no extractable data)
  • β€’ Multiple plots in single figure
  • β€’ Legends separate from chart area
  • β€’ Axis labels at various angles
  • β€’ Overlapping data series

πŸ”¬Scientific Notation

  • β€’ Superscripts and subscripts
  • β€’ Mathematical symbols (Β±, ≀, β‰₯)
  • β€’ Special characters (Ξ±, Ξ², ΞΌ)
  • β€’ Chemical formulas
  • β€’ Statistical annotations

πŸ“Layout Complexity

  • β€’ Multi-column layouts
  • β€’ Text wrapping around figures
  • β€’ Footnotes and endnotes
  • β€’ Headers and footers with metadata
  • β€’ Landscape and portrait mixed pages

Real-World Example: Stability Study Table

A typical ICH stability study table contains:

  • 1.
    Hierarchical Headers:
    Time Point (months) β†’ Storage Condition β†’ Test Parameter β†’ Specification β†’ Results
  • 2.
    Merged Cells:
    "0, 3, 6, 9, 12, 18, 24, 36 months" spans entire row, "25Β°C/60% RH" spans multiple columns
  • 3.
    Scientific Notation:
    "β‰₯95.0%" for assay, "≀2.0%" for degradation products, "pH 6.8 Β± 0.3"
  • 4.
    Footnotes:
    Asterisks (*) linking to study conditions, batch numbers, and analytical methods below table

Traditional OCR Result: Extracts text as unstructured lines, loses all table structure, cannot associate headers with data cells, misses merged cell relationships β†’ Unusable output

Multi-Modal AI Result: Understands table structure, preserves all relationships, correctly interprets merged cells, maintains header hierarchy β†’ 98%+ structural fidelity

Vision-Language Model Architecture

Modern multi-modal AI models use a sophisticated architecture that processes visual and textual information in parallel, then fuses them for comprehensive understanding.

Core Components

1

Vision Encoder

Processes the document image at pixel level to extract visual features

  • β€’ Converts image into patch embeddings (typically 16x16 or 32x32 pixels)
  • β€’ Applies convolutional neural networks or vision transformers
  • β€’ Captures spatial relationships, layouts, visual patterns
  • β€’ Creates high-dimensional feature maps preserving positional information
2

Text Encoder

Processes extracted text to understand linguistic meaning and context

  • β€’ Tokenizes text into subword units
  • β€’ Generates contextual embeddings using transformer architecture
  • β€’ Understands semantic relationships between words and phrases
  • β€’ Captures domain-specific terminology and abbreviations
3

Multi-Modal Fusion Layer

Combines visual and textual representations for unified understanding

  • β€’ Cross-attention mechanisms link visual patches to text tokens
  • β€’ Learns which visual features correspond to which text elements
  • β€’ Aligns spatial positions with semantic meaning
  • β€’ Creates joint embeddings that capture both modalities
4

Decoder / Generation Layer

Produces structured output based on fused multi-modal understanding

  • β€’ Generates structured representations (JSON, Markdown, HTML)
  • β€’ Preserves table structure with proper row/column relationships
  • β€’ Maintains formatting information (bold, italics, alignment)
  • β€’ Outputs semantic annotations and metadata

Training Paradigm

Multi-modal models are trained in multiple stages to develop comprehensive document understanding:

Stage 1: Pre-training on General Vision-Language Tasks

Models learn basic visual-linguistic alignment from millions of image-text pairs (e.g., image captions, visual question answering). This builds foundational understanding of how visual elements relate to textual descriptions.

Stage 2: Document-Specific Fine-tuning

Continued training on document images paired with ground-truth structured outputs. Models learn to recognize tables, charts, figures, and extract them with high fidelity. Includes diverse document types: scientific papers, financial reports, forms, invoices.

Stage 3: Domain Adaptation (Optional)

Further refinement on domain-specific documents (e.g., regulatory submissions, medical records). This stage is often done by end users on proprietary data to maximize accuracy for their specific use case.

How Multi-Modal AI "Sees" Documents

Understanding the model's perception process is key to leveraging its capabilities effectively.

Visual Processing Pipeline

  1. Step 1:
    Image Ingestion: Document page rendered as high-resolution image (typically 300 DPI minimum)
  2. Step 2:
    Patch Extraction: Image divided into overlapping patches (e.g., 16x16 pixel squares with 50% overlap)
  3. Step 3:
    Feature Embedding: Each patch converted to high-dimensional vector (768-1024 dimensions)
  4. Step 4:
    Spatial Encoding: Positional information added so model knows where each patch is on page
  5. Step 5:
    Attention Mechanism: Model learns which patches are related (e.g., header cells in same table)
  6. Step 6:
    Structural Recognition: Patterns identified: table grids, chart axes, figure boundaries, text blocks

What the Model "Understands"

βœ“ Visual Patterns

  • β€’ Grid lines indicating table boundaries
  • β€’ Cell borders (solid, dashed, or invisible)
  • β€’ Shading and background colors
  • β€’ Font sizes (headers typically larger)
  • β€’ Text alignment (left, center, right)
  • β€’ Bold/italic formatting
  • β€’ Whitespace patterns separating sections

βœ“ Semantic Patterns

  • β€’ Table headers vs. data cells (based on content)
  • β€’ Numeric patterns (percentages, ranges, p-values)
  • β€’ Units of measurement
  • β€’ Repeated structures (row/column patterns)
  • β€’ Hierarchical relationships (nested headers)
  • β€’ Caption text ("Table 1:", "Figure 2:")
  • β€’ Footnote markers (*, †, ‑, a, b, c)

Spatial Reasoning Capabilities

One of the most powerful aspects of multi-modal AI is spatial reasoningβ€”the ability to understand geometric relationships between elements:

  • β†’
    Above/Below Relationships: Understands that text directly above a table is likely its caption
  • β†’
    Left/Right Alignment: Recognizes that aligned elements likely belong to same column
  • β†’
    Containment: Knows that cells are contained within tables, text within cells
  • β†’
    Spanning: Identifies when a cell spans multiple rows or columns based on border patterns
  • β†’
    Grouping: Clusters related elements even without explicit borders (e.g., footnotes below table)

Table Extraction Deep Dive

Table extraction is the most demanding task for document intelligence systems. Let's examine how multi-modal AI achieves 95%+ accuracy where traditional methods fail.

The Table Structure Problem

A table is not just text arranged in a grid. It's a complex hierarchical data structure with:

  • β€’ Logical structure: Rows and columns forming a matrix
  • β€’ Header hierarchy: Multi-level headers creating tree relationships
  • β€’ Cell relationships: Data cells linked to all relevant headers
  • β€’ Merged cells: Single cells spanning multiple logical positions
  • β€’ Metadata: Captions, footnotes, units, statistical annotations

Multi-Modal Extraction Process

1Table Detection

First, the model identifies table regions on the page:

  • β€’ Recognizes visual patterns: grid lines, cell borders, regular spacing
  • β€’ Distinguishes tables from other structured elements (lists, forms)
  • β€’ Determines table boundaries (where table starts and ends)
  • β€’ Handles edge cases: borderless tables, tables within text flow

2Row & Column Identification

Next, logical grid structure is inferred:

  • β€’ Detects horizontal separators (borders, whitespace) defining rows
  • β€’ Detects vertical separators defining columns
  • β€’ Handles irregular grids (varying row heights, column widths)
  • β€’ Manages merged cells that span multiple rows/columns

3Header Classification

Distinguishes headers from data cells:

  • β€’ Visual cues: Bold text, larger font, shaded background, centered alignment
  • β€’ Positional cues: Top rows and left columns more likely to be headers
  • β€’ Content cues: Generic labels ("Parameter", "Result") vs. specific values
  • β€’ Builds header hierarchy for multi-level headers

4Cell Content Extraction

Reads text within each cell:

  • β€’ OCR with context awareness (knows expected content type)
  • β€’ Handles special characters, superscripts, subscripts
  • β€’ Preserves formatting (bold, italics, underline)
  • β€’ Detects empty cells vs. cells with no visible borders

5Relationship Mapping

Establishes connections between cells:

  • β€’ Links each data cell to its row and column headers
  • β€’ For multi-level headers, creates full header path for each cell
  • β€’ Associates footnote markers with corresponding footnote text
  • β€’ Connects table to its caption and any related figures

6Structure Serialization

Outputs structured representation:

  • β€’ JSON: Nested object structure with full metadata
  • β€’ Markdown: Human-readable table syntax
  • β€’ HTML: Semantic markup with proper th/td elements
  • β€’ CSV: Flattened tabular format (with header expansion)

Handling Edge Cases

Common challenges and how multi-modal AI addresses them:

Borderless Tables: Uses alignment patterns and whitespace to infer cell boundaries even without visible borders
Rotated Text: Vision encoder detects rotation angle and adjusts reading direction accordingly
Nested Tables: Hierarchical parsing identifies parent-child table relationships and preserves nesting structure
Split Tables: Recognizes when table continues across pages by matching column headers and structure
Irregular Grids: Adapts to non-rectangular layouts where cells have varying sizes and positions

Chart & Figure Analysis

Unlike tables where text is embedded in the PDF, charts and figures are often raster images. Multi-modal AI can still extract meaningful information through visual analysis.

What Can Be Extracted from Charts

Metadata Extraction

  • βœ“ Chart title and subtitle
  • βœ“ Axis labels and units
  • βœ“ Legend items and colors
  • βœ“ Data series names
  • βœ“ Caption text
  • βœ“ Source attribution
  • βœ“ Statistical annotations (p-values, error bars)

Visual Understanding

  • βœ“ Chart type (line, bar, scatter, box plot, etc.)
  • βœ“ Number of data series
  • βœ“ Approximate data ranges
  • βœ“ Trends and patterns
  • βœ“ Peak/valley identification
  • βœ“ Comparative relationships
  • βœ“ Outlier detection

Note: While multi-modal AI can understand chart structure and read text labels, precise data point extraction from raster images is limited. For exact values, source data files or digitization tools are needed.

Figure Caption Association

A critical capability is linking figures to their captions and related text:

  1. 1.
    Caption Detection: Identifies text beginning with "Figure X:", "Fig. X", or similar patterns
  2. 2.
    Proximity Analysis: Associates caption with nearest image based on spatial distance and layout
  3. 3.
    Numbering Validation: Ensures figure numbers are sequential and match between caption and in-text references
  4. 4.
    Related Text Extraction: Finds paragraphs referring to the figure (e.g., "As shown in Figure 3...")

Chart Type Recognition

Multi-modal AI can classify charts into standard types, enabling appropriate handling:

Line Charts

Trends over time, pharmacokinetic profiles

Bar Charts

Categorical comparisons, efficacy endpoints

Scatter Plots

Correlations, dose-response relationships

Box Plots

Distribution summaries, statistical comparisons

Kaplan-Meier

Survival analysis, time-to-event data

Forest Plots

Meta-analysis, subgroup analyses

Model Comparison & Selection

Several multi-modal AI models are available for document intelligence. Each has strengths and trade-offs.

Leading Models (November 2025)

🟣

Claude

Anthropic

Strengths
  • β€’ Excellent table structure preservation (98%+ accuracy)
  • β€’ Superior handling of merged cells and nested headers
  • β€’ Strong understanding of scientific notation
  • β€’ Reliable footnote association
  • β€’ Large context window (200K tokens)
Considerations
  • β€’ Higher cost per API call vs. competitors
  • β€’ Slightly slower inference (2-4 seconds per page)
  • β€’ Rate limits on concurrent requests
Best for: Complex regulatory tables, multi-level headers, high-stakes accuracy requirements
πŸ”΅

Gemini

Google

Strengths
  • β€’ Fast inference (1-2 seconds per page)
  • β€’ Lower cost per request
  • β€’ Excellent chart type recognition
  • β€’ Strong visual reasoning for layouts
  • β€’ Native integration with Google Cloud
Considerations
  • β€’ Occasional issues with deeply nested tables
  • β€’ Less consistent with footnote markers
  • β€’ May struggle with borderless tables
Best for: High-volume processing, chart-heavy documents, cost-sensitive applications
🟒

OpenAI GPT

OpenAI

Strengths
  • β€’ Broad availability and ecosystem support
  • β€’ Good general-purpose vision capabilities
  • β€’ Flexible output formatting
  • β€’ Strong reasoning about document context
Considerations
  • β€’ Best suited for standard document formats
  • β€’ Complex nested tables may require validation
  • β€’ Moderate speed and cost
Best for: Simple documents, broad use cases, existing OpenAI infrastructure

Selection Criteria

Choose your model based on these factors:

β†’
Document Complexity: Complex regulatory tables β†’ Claude; Simpler documents β†’ Gemini
β†’
Volume & Cost: High volume β†’ Gemini (lower cost); Lower volume, high stakes β†’ Claude (higher accuracy)
β†’
Latency Requirements: Real-time needs β†’ Gemini (faster); Batch processing β†’ Claude (quality over speed)
β†’
Hybrid Approach: Use Claude for critical tables, Gemini for charts/figures to optimize cost-quality balance

Quality Assurance & Validation

Even with 95%+ accuracy, validation is essential for regulatory submissions. Here's how to ensure quality.

Multi-Layer Validation Strategy

L1Structural Validation

  • β€’ Verify table has expected number of rows and columns
  • β€’ Check that all cells are populated (or intentionally empty)
  • β€’ Validate header hierarchy is complete
  • β€’ Ensure merged cells are properly represented

L2Content Validation

  • β€’ Check data types match expectations (numeric cells contain valid numbers)
  • β€’ Validate units are preserved
  • β€’ Verify special characters (≀, Β±, etc.) are correct
  • β€’ Confirm footnote markers match footnote text

L3Semantic Validation

  • β€’ Cross-check extracted data against expected ranges
  • β€’ Validate statistical consistency (e.g., mean within min-max range)
  • β€’ Check temporal ordering (month 0 before month 6)
  • β€’ Verify relationships (total equals sum of components)

L4Visual Comparison

  • β€’ Side-by-side comparison of original PDF and extracted table
  • β€’ Highlight differences for human review
  • β€’ Flag low-confidence extractions for verification
  • β€’ Generate quality score based on validation results

Confidence Scoring

Assign confidence scores to extracted elements based on multiple factors:

High Confidence (90-100%): Clear table borders, consistent formatting, all cells readable, proper alignment
Medium Confidence (70-89%): Some borderless cells, minor OCR ambiguities, slightly irregular spacing
Low Confidence (<70%): Complex nested structure, poor image quality, unusual formatting, partially obscured text

Best Practice: Flag all low-confidence extractions for mandatory human review before inclusion in regulatory submissions.

Performance Optimization

Optimizing multi-modal AI pipelines involves balancing accuracy, speed, and cost.

Image Preprocessing

Proper image preparation significantly improves extraction quality:

  • βœ“
    Resolution: Render PDFs at 300 DPI minimum for clear text and borders
  • βœ“
    Contrast Enhancement: Adjust contrast for faded tables or low-quality scans
  • βœ“
    Deskewing: Correct skewed pages to improve alignment detection
  • βœ“
    Noise Reduction: Remove artifacts from scanning or compression
  • βœ“
    Format Standardization: Convert to PNG or JPEG (avoid TIFF for API calls)

Prompt Engineering

Well-crafted prompts dramatically improve extraction accuracy and consistency:

Effective Prompt Structure

  • β€’ Specify exact output format (JSON schema, Markdown table syntax)
  • β€’ Define handling rules for edge cases (empty cells, merged cells)
  • β€’ Request metadata (caption, footnotes, units)
  • β€’ Ask for confidence scores on ambiguous elements
  • β€’ Provide examples of desired output format

Domain-Specific Instructions

  • β€’ Include regulatory context (ICH stability study, clinical endpoint table)
  • β€’ Specify expected headers or column structure when known
  • β€’ Define abbreviations and terminology specific to your domain
  • β€’ Request preservation of scientific notation and special characters

Batch Processing & Caching

Batch Optimization

  • β€’ Group similar documents to leverage model's learned patterns
  • β€’ Process pages in parallel with rate limit management
  • β€’ Prioritize critical tables for immediate processing
  • β€’ Queue low-priority extractions for off-peak hours

Intelligent Caching

  • β€’ Hash document pages to detect duplicates
  • β€’ Cache extraction results keyed by image hash
  • β€’ Reuse extractions for recurring table templates
  • β€’ Invalidate cache only when source document changes

Cost Management

Strategies to minimize API costs while maintaining quality:

  • β€’ Use cheaper models for simple tables, reserve premium models for complex ones
  • β€’ Implement selective extraction (only extract tables/charts, skip plain text)
  • β€’ Compress images to minimum acceptable quality (balance quality vs. size)
  • β€’ Monitor extraction quality and adjust model selection dynamically
  • β€’ Set up spending alerts and quotas to prevent cost overruns

Best Practices & Limitations

Implementation Best Practices

βœ“Start with High-Quality Sources

Digital PDFs (not scans) produce best results. If working with scans, ensure 300+ DPI resolution and clean contrast. Avoid faxed or photocopied documents when possible.

βœ“Establish Ground Truth Dataset

Manually verify 50-100 representative tables to establish accuracy baseline. Use these as test cases when evaluating models or tuning prompts.

βœ“Implement Human-in-the-Loop Review

Never fully automate critical regulatory submissions. Flag low-confidence extractions for human review. Use AI to accelerate, not replace, expert oversight.

βœ“Version Control Extractions

Track which model version and prompt were used for each extraction. This enables traceability and rollback if issues are discovered later.

βœ“Monitor and Iterate

Continuously measure accuracy on new documents. As models improve or document types evolve, update prompts and validation rules accordingly.

Known Limitations

⚠Heavily Degraded Documents

Low-resolution scans (<150 DPI), faded text, or significant noise can reduce accuracy to 70-80%. Pre-processing helps but cannot fully compensate for poor source quality.

⚠Extremely Complex Nested Tables

Tables with 5+ levels of header hierarchy or deeply nested sub-tables may have 5-10% error rate. Consider simplifying table structure or manual verification.

⚠Handwritten Annotations

Handwritten notes on printed tables are inconsistently recognized. For critical handwritten content, manual entry or specialized handwriting recognition may be needed.

⚠Precise Data Point Extraction from Charts

While chart metadata is accurately extracted, precise numerical values from raster chart images have limited accuracy (Β±5-10%). Use source data files when exact values are required.

⚠Non-Latin Scripts

Most models are optimized for Latin alphabets. Documents in Chinese, Japanese, Arabic, or other scripts may require specialized models or additional validation.

Future Directions

The field of multi-modal AI is rapidly evolving:

  • β†’
    Improved Chart Data Extraction: Next-generation models will extract precise data points from chart images with 95%+ accuracy
  • β†’
    Multi-Page Table Handling: Better detection and stitching of tables that span multiple pages
  • β†’
    Interactive Extraction: Models that can ask clarifying questions when structure is ambiguous
  • β†’
    Domain Fine-Tuning: Specialized models trained specifically on regulatory documents for even higher accuracy
  • β†’
    Real-Time Processing: Sub-second extraction times enabling interactive document analysis

Ready to Implement Multi-Modal AI?

See how DossiAIr uses multi-modal AI to extract regulatory tables with 95%+ accuracy