Beyond Text: Extracting Tables, Forms & Images with Grepsr’s LLM + OCR Pipelines

While textual content forms the backbone of many PDF documents, enterprise PDFs increasingly contain tables, forms, and images that carry essential information. Extracting only the text often results in incomplete datasets, lost context, and limited insights.

Grepsr addresses this challenge with LLM-enhanced OCR pipelines designed to handle multi-modal PDF content, enabling enterprises to transform complex PDFs into structured, actionable data efficiently and accurately.

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The Complexity of Multi-Modal PDFs

Enterprise PDFs often include:

1. Tables – Financial statements, inventory lists, or research results.
2. Forms – Surveys, applications, and regulatory submissions with structured fields.
3. Images and Diagrams – Product schematics, charts, or scanned signatures.
4. Mixed Content – PDFs combining text, tables, and graphics on the same page.

Manual extraction of these elements is time-consuming and error-prone, while traditional tools often fail to maintain the semantic relationships between text, tables, and images.

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Grepsr’s LLM + OCR Approach

Grepsr combines optical character recognition (OCR) with large language models (LLMs) to understand content in context:

1. OCR for Scanned and Image-Based Content

• Extracts textual content from scanned PDFs and embedded images.
• Detects text orientation, font variations, and embedded graphics.
• Enterprise benefit: Ensures no critical information is missed in image-heavy documents.

2. Table Detection and Parsing

• Identifies tables using layout analysis and semantic understanding.
• LLMs interpret column headers, row relationships, and embedded notes.
• Enterprise benefit: Transforms complex tables into clean, structured datasets ready for analytics or database integration.

3. Form Field Extraction

• Recognizes checkboxes, radio buttons, and text fields.
• Maps extracted data to predefined schemas or dynamic labels.
• Enterprise benefit: Streamlines surveys, applications, and regulatory submissions into structured formats.

4. Image and Diagram Interpretation

• Detects charts, graphs, and visual diagrams.
• LLMs assist in contextual interpretation, linking images with adjacent text or table data.
• Enterprise benefit: Captures insights from non-textual content that traditional parsing tools miss.

5. Context-Aware Integration

• Combines extracted text, tables, forms, and images into coherent datasets.
• Preserves relationships between elements, e.g., a table caption linked to corresponding chart data.
• Enterprise benefit: Provides a holistic, actionable dataset for enterprise analytics, AI training, or reporting.

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Applications Across Enterprises

Financial Analysis

• Extract tables from annual reports, balance sheets, and expense summaries.
• Capture embedded notes and diagrams for complete financial context.

Legal & Contract Management

• Parse forms and clauses within contracts.
• Extract tables detailing obligations, payment terms, and schedules.

Healthcare & Clinical Trials

• Extract patient forms, lab results, and imaging diagrams.
• Combine text, tables, and visuals for comprehensive research datasets.

Regulatory Reporting

• Automate extraction of structured data from compliance documents.
• Preserve relationships between tables, form fields, and textual instructions.

Supply Chain & Inventory Management

• Extract invoices, manifests, and shipping forms.
• Capture tables, embedded images, and annotated diagrams for accurate record-keeping.

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Technical Architecture for Multi-Modal PDF Extraction

1. Ingestion Layer – Collects PDFs from multiple sources including email, portals, and cloud storage.
2. Preprocessing Layer – Detects document types and identifies text vs image pages.
3. OCR Layer – Converts scanned images to machine-readable text while detecting layout and orientation.
4. LLM Processing Layer – Interprets tables, forms, and images, maintaining context.
5. Validation Layer – Ensures extracted content aligns with enterprise schemas and detects errors.
6. Integration Layer – Outputs structured datasets for ERP, analytics, or AI pipelines.

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Case Example: Extracting Multi-Modal PDFs in Financial Services

A multinational bank processes thousands of regulatory filings containing tables, forms, and scanned charts:

• Grepsr applied OCR to image-based filings.
• LLMs extracted tables and linked captions and diagrams.
• Form fields were automatically mapped to compliance schemas.
• Result: Extraction accuracy exceeded 97%, with time-to-data reduced by 60% and compliance reporting automated efficiently.

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Benefits of Grepsr’s Multi-Modal Extraction

• Comprehensive Data Capture – Text, tables, forms, and images captured together.
• High Accuracy – LLMs provide context-aware parsing and semantic interpretation.
• Scalable Processing – Thousands of PDFs processed daily without manual intervention.
• Actionable Insights – Structured data ready for analytics, reporting, or AI applications.
• Reduced Manual Work – Minimizes human effort while maintaining reliability.

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Best Practices for Multi-Modal PDF Extraction

1. Combine OCR with LLM Understanding – Ensure accurate extraction of both text and layout.
2. Preserve Relationships Between Elements – Tables, forms, and images should remain linked to context.
3. Validate Extracted Data Against Schemas – Apply error detection and corrections.
4. Automate at Scale – Use pipelines for high-volume processing without manual bottlenecks.
5. Monitor Performance – Track extraction accuracy, errors, and anomalies for continuous improvement.

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Beyond Text, Unlocking Complete PDF Insights

Grepsr’s LLM + OCR pipelines enable enterprises to go beyond text, extracting tables, forms, and images from complex PDFs. By combining AI-driven context understanding with multi-modal parsing, organizations gain comprehensive, accurate, and actionable datasets. This approach reduces manual effort, accelerates analytics, and ensures enterprise-grade insights from even the most challenging PDF documents.

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