Fujifilm Data Management Solutions

Intelligent Document Processing

Applying Content AI to transform documents into structured, operational information

Intelligent Document Processing with Content AI

Intelligent Document Processing supports organisations that need to turn large volumes of documents into reliable, system-ready information.

These capabilities combine Optical Character Recognition (OCR), machine learning and AI-assisted analysis to extract, classify, validate and protect information within documents at scale.

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How Intelligent Document Processing works

This is the technical execution layer of Content AI. The pipeline combines multiple AI techniques with governed workflows to extract, understand, classify and protect documents at scale.

OCR, machine learning, rules-based logic and confidence scoring are applied across defined processing stages to prepare documents for operational use. Validation and review are triggered where required, ensuring automation is applied appropriately while maintaining oversight, auditability and control.

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Upload

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OCR

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Classify

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Output

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Redact

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Detect PII

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Upload

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OCR

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Classify

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Output

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Redact

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Detect PII

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Upload

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OCR

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Classify

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Detect PII

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Redact

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Output

This approach applies automation selectively with validation and review used where content or risk requires it.

Technical execution behind the pipeline

While presented as a logical workflow, processing operates through adaptive, policy-driven execution paths that vary by document type, confidence and risk profile.


Upload

Documents are ingested from physical and digital sources including scanned records, PDFs, images and electronic submissions.

Preparation steps may be applied to improve readability and consistency, ensuring content is ready for accurate extraction and validation.


OCR and text extraction

AI-assisted OCR extracts printed text, handwriting, tables and form data while preserving document layout.

Extraction is designed to handle real-world variability including mixed-quality scans, complex layouts and inconsistent orientation, ensuring outputs remain suitable for validation, protection and downstream integration.


Classification

Machine learning models analyse document content, structure and contextual signals to identify document types. Classification determines routing, validation rules and downstream handling.

Classification behaviour is governed through configurable policies combining content indicators, structural patterns and confidence thresholds.


Sensitive data detection and protection

Sensitive data detection is applied using different techniques depending on document type, context and risk profile.

Detection may operate at entity, pattern or document level, with redaction or masking applied according to policy requirements.


Validation and review

Confidence scoring is applied at document and field level to assess output reliability. High confidence results proceed automatically while lower confidence or higher risk content is routed for structured human review.

Manual review is applied selectively, ensuring effort is focused only where required without disrupting throughput.


Output

Processed documents and extracted data are prepared for operational use, including searchable PDF or PDF/A and structured data formats such as JSON or XML. Outputs are aligned to records management, storage and system integration requirements.

Underlying document analysis capabilities

Document processing is underpinned by machine learning-based analysis engines designed to handle complex, real-world content rather than simple text capture. These capabilities support a wide range of document structures, formats and content variability encountered across operational workflows.


Advanced text extraction

Extract printed text, handwriting, tables and form fields while preserving layout relationships and context rather than flattening content into plain text.


Multi-format document handling

Process mixed document inputs such as PDF, PDF/A, TIFF, JPEG and PNG together within the same workflow, without manual pre-sorting or format-specific handling.


Structure-aware extraction

Retain positional and contextual information required for validation, sensitive data handling and downstream system integration.


Policy-driven sensitive data handling

Apply redaction or masking based on defined policies, allowing sensitive information to be protected in line with privacy, security and use-case requirements.

Processing depth and accuracy control

The processing model is designed to handle real-world variability in document types, layouts and quality while maintaining accuracy at scale.

Processing logic and validation controls operate at both document and field level, allowing different content types and risk profiles to be handled within the same workflow without compromising reliability or oversight.


Document handling and extraction logic

The processing framework supports a wide range of document structures and scenarios, enabling consistent outcomes even when document quality or formats vary.

  • Structured, semi-structured and unstructured documents are analysed and processed using document-specific logic
  • Mixed document batches can be processed together, with classification determining the appropriate extraction path for each document
  • Field-level and document-level metadata is extracted based on layout, content patterns and contextual rules
  • Extracted outputs are normalised into consistent, system-ready formats to support downstream use
  • This approach allows large and diverse document sets to be processed without manual pre-sorting or rigid format constraints.


Validation and confidence-based control

Accuracy is managed through confidence-led controls rather than fixed automation rules, allowing workflows to adapt to document complexity and risk.

  • Confidence scores are generated at field and document level to indicate reliability of extracted data
  • Thresholds determine when outputs can be accepted automatically and when validation is required
  • Partial failures are handled in a controlled way, allowing high-confidence fields to pass while isolating exceptions
  • Manual effort is focused only where needed, improving efficiency without sacrificing accuracy
  • This model prevents over-automation while still enabling scale.

System-ready outputs

Processed documents and extracted data can be stored, searched, integrated and reused without additional rework.


Archival and document outputs

Searchable, standards-aligned document outputs designed for long-term use and compliance.

  • Searchable and archival-ready PDF/A outputs
  • Supports long-term retention and audit requirements
  • Consistent document structure for downstream access


Structured data outputs

Extracted data prepared in machine-readable formats to support automation and system processing.

  • Structured formats such as JSON and XML
  • Field-level normalisation and consistency
  • Suitable for automation and analytics pipelines


Indexing and metadata files

Supporting files that enable efficient storage, retrieval and records management.

  • Index and metadata files aligned to content platforms
  • Supports classification, search and retrieval
  • Designed to integrate with records management frameworks


Downstream system integration

Outputs prepared to flow directly into operational systems without manual rework.

  • Payloads aligned to downstream system requirements
  • Supports business applications, analytics and workflow tools
  • Reduces hand-offs between digitisation and operations

Governance, traceability and audit support

Governance is embedded across the processing workflow to ensure decisions remain transparent, traceable and accountable.

Traceable processing and decision lineage

Every document follows a defined and traceable path from intake through to output. Processing steps, classification decisions and extraction outcomes are recorded to support visibility, investigation and assurance.

Validation, review and accountability

Where review is required, validation and correction are applied through controlled workflows. Human actions are recorded alongside automated outcomes, maintaining accountability without disrupting throughput.

Compliance and records alignment

Intelligent Document Processing outputs align with records management, retention and audit requirements, enabling digitised content to be trusted, retained and reused within regulated operating models.

These controls are enforced through the broader Content AI technical architecture, which governs processing, validation and auditability at scale.

View Content AI technical architecture

Discuss your Intelligent Document Processing needs

If you’re looking to improve document capture, extraction, validation or system-ready outputs, share a few details below. We’ll review your document types, volumes and requirements and get in touch to discuss a practical Intelligent Document Processing approach.

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