Capture-for-AI: Four Building Blocks for Making Information AI-Ready
Artificial intelligence is changing how organizations think about information.
For decades, document capture was primarily about getting paper into a digital format. A document was scanned, converted into an image or PDF, and sent into a business process.
AI changes the equation.
An AI system doesn't simply need access to a document. It needs information that can be captured accurately, preserved reliably, authenticated, and transformed into a representation that machines can understand.
That means we need to think about what happens between the physical document and the AI model.
I believe there are four distinct building blocks for a Capture-for-AI and I'll use the below architecture as an example (although there are many other great alternatives):
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TWAIN Direct — Capture the image
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PDF/R — Preserve and transport the captured image as a standardized file
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C2PA — Establish authenticity and provenance
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DocLang — Transform the document into an AI-ready representation
These are not four versions of the same thing.
They solve four different problems.
Capture. File. Authenticity. Transformation.
Together, they provide a path from the physical world to AI.
1. TWAIN Direct: Capture the Image
Let's start at the beginning.
A physical document has to become digital information.
That is where TWAIN Direct fits.
TWAIN Direct is fundamentally an image-capture technology. It provides a standardized way for applications to communicate with image acquisition devices.
It is important to be precise about this.
TWAIN Direct captures images. It does not attempt to understand the document.
It doesn't determine that a page is an invoice, identify a purchase-order number, understand a table, or interpret the meaning of a paragraph.
Its job is much more fundamental:
Get the image from the capture device into the application reliably and with the quality necessary for everything downstream.
That distinction matters enormously for AI.
The AI model cannot recover information that was never captured.
Scanning-for-AI FAQ: What resolution should I use?
There isn't one resolution that is appropriate for every document.
But 300 dpi is a useful baseline for many business-document capture applications.
Higher resolution can be appropriate for:
The objective shouldn't be "capture everything at the highest possible resolution." For example, Terms & Services text might be completely unneccessary for many use cases.
The objective should be:
Capture enough information to reliably support the intended downstream use.
Does the camera or scanner really matter if AI is doing the recognition?
Yes.
AI does not eliminate the importance of the imaging system.
Optics, sensor quality, lighting, focus, exposure, dynamic range, distortion, paper handling, and image resolution all affect the source image.
A good CCD or CMOS imaging system can provide significantly better source material than a poorly designed camera system.
The AI system can interpret an image.
It cannot invent detail that wasn't captured.
What about image processing?
Image processing is an important part of the capture pipeline.
Depending on the application, that can include:
The goal isn't simply to make the image look better.
The goal is to create a high-quality digital image that downstream systems can reliably process.
Why does interoperability matter?
Organizations don't have just one way of acquiring information.
Information can arrive from:
A standardized capture interface can reduce the amount of device-specific engineering required to connect those sources to applications.
That is the role of TWAIN Direct.
It gets the image into the digital pipeline.
But an image is not yet a standardized document file.
That's the next layer.
2. PDF/R: Preserve, Transport, and Exchange the Captured Image
Once an image has been captured, we need a way to package and preserve it as a document.
This is where PDF/R comes in.
PDF/R is a file format and an international standard.
Specifically, PDF/R is ISO 23504, which defines a subset of PDF for the storage, transport, and exchange of multi-page raster-image documents, including scanned documents. It supports bitonal, grayscale, and RGB images and defined compression options. (ISO)
That distinction is important:
TWAIN Direct is about image capture.
PDF/R is about putting those captured images into a standardized, portable file.
The PDF Association describes PDF/R as a strict subset of PDF designed specifically for storing, transporting, and exchanging multi-page raster-image documents. Because it is a restricted subset of PDF, it is simpler to generate and interpret while taking advantage of the broad ecosystem of PDF viewing, printing, and processing tools. (PDF Association)
That gives PDF/R an important role in Capture-for-AI.
A standard matters
One of the biggest problems with information that needs to survive for years—or move between systems—is dependence on proprietary implementations.
A standard creates a common technical contract.
PDF/R isn't simply:
"Let's put some scanner images inside a PDF."
It is an ISO-standardized specification for how this particular class of information should be represented.
ISO 23504 defines PDF/R-1 as an international standard based on ISO 32000 and specifically addresses storage, transport, and exchange. (ISO)
That matters because information doesn't necessarily remain inside the system that created it.
Documents may need to move between:
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Capture applications
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Content-management systems
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Records-management systems
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Business applications
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Cloud repositories
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Archives
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AI pipelines
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Different vendors
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Different generations of software
A standardized file format provides a common language for that exchange.
Transportability is a feature—not an afterthought
The ability to transport and exchange a captured document is particularly important in an AI environment.
A capture application shouldn't have to know which AI system will ultimately consume the information.
Likewise, an AI application shouldn't have to be tightly coupled to the scanner that originally captured the document.
The standardized file becomes an intermediate information object.
That creates an architecture such as:
Scanner → TWAIN Direct → PDF/R → Repository / AI pipeline
The scanner and capture application can evolve independently of the systems that eventually consume the file.
That's interoperability.
And interoperability is one of the foundational ideas behind the modern information-management ecosystem.
What about long-term preservation?
This is where terminology matters.
PDF/R is designed for storage, transport, and exchange of raster-image documents. It is not itself the same thing as PDF/A, the ISO family specifically focused on long-term preservation.
For organizations concerned with records that must remain accessible over very long periods, the appropriate archival strategy may involve PDF/A and other preservation controls in addition to the use of PDF/R in the capture and transport workflow.
The important point is that Capture-for-AI shouldn't create disposable information.
The captured image may be the primary digital representation of a physical document, and organizations may need to preserve that source representation for years or decades.
A standardized, portable representation helps make that possible.
Data-rich metadata matters
There is another important reason to think about the file as an information object rather than merely a picture.
Metadata is information.
The PDF Association's AI and PDF guidance points out that PDF metadata—including document-level XMP and object-level metadata—can provide information about a document and its contents and can influence how AI systems understand and process PDFs. (PDF Association)
That means a Capture-for-AI pipeline should think beyond the pixels.
The file can carry information about the document, its origin, its characteristics, and its relationship to the surrounding business process.
So the progression is not simply:
Image → File
It is:
Captured image → Standardized document file + metadata
That becomes a much more valuable object for everything that follows.
3. C2PA: Establish Authenticity and Provenance
Now we have a high-quality image represented in a standardized file.
But another question emerges:
Can we trust it?
This is where C2PA enters the architecture.
The C2PA specifications define technical standards for certifying the source and history, or provenance, of media content. (C2PA)
C2PA is therefore not the capture layer.
It is not the file format.
And it is not the AI transformation layer.
Its role is:
Authenticity and provenance.
C2PA Content Credentials provide a mechanism for associating verifiable provenance information with digital content, including information about its origin and subsequent actions or transformations. (C2PA)
That becomes increasingly important as AI systems consume information without a person examining every document first.
Imagine a document entering an AI workflow.
The AI system may eventually:
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Extract information
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Classify it
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Summarize it
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Compare it with other information
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Make a recommendation
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Trigger an automated process
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Give instructions to an AI agent
Wouldn't it be useful to know something about the document's history?
Where did it come from?
What captured it?
When was it captured?
Has it been modified?
What processing has been performed?
C2PA provides a standardized framework for recording that provenance.
Does C2PA prove that the information is true?
No.
This is an important distinction.
C2PA does not determine whether the underlying information is factually true.
Instead, it provides verifiable information about provenance and the history of the digital content.
Provenance is not the same thing as truth.
But knowing provenance can be extremely valuable when deciding how much confidence to place in information.
4. DocLang: Transform the Document for AI
The fourth component is fundamentally different from the first three.
DocLang is the transformation layer.
DocLang is described by ABBYY as an open document standard for representing a document's structure, meaning, and layout in a machine-readable format optimized for AI consumption. It is intended to provide a common representation that can feed downstream AI models, agents, analytics, and compliance systems. (ABBYY)
This addresses a major problem in today's AI implementations.
A PDF can contain a tremendous amount of information about a document.
But an AI application still needs to understand that information.
The transformation layer converts the document from its original representation into a form optimized for machine consumption.
That can preserve concepts such as:
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Document structure
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Reading order
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Semantic roles
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Tables
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Figures
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Relationships
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Layout
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Governance metadata
ABBYY describes DocLang as an AI-native document standard that explicitly encodes semantic roles and reading order while preserving tables, figures, relationships, and governance information. (ABBYY)
This is an important distinction from simply extracting text.
The objective is to transform the document into an AI-understandable representation without unnecessarily throwing away the structure and meaning contained in the original.
The Four Components Are Different—And That's the Point
This is perhaps the most important part of the Capture-for-AI concept.
| Component |
Primary role |
| TWAIN Direct |
Capture — acquire the image from the physical source |
| PDF/R |
File — standardize, store, transport, and exchange the raster document |
| C2PA |
Authenticity — establish provenance and history |
| DocLang |
Transformation — represent the document's structure, meaning, and layout for AI |
They form a sequence:
1. CAPTURE
TWAIN Direct
Acquire a high-quality image.
↓
2. FILE
PDF/R
Create a standardized, portable document file.
↓
3. AUTHENTICITY
C2PA
Establish provenance and authenticity information.
↓
4. TRANSFORMATION
DocLang
Transform the document into a machine-readable representation optimized for AI.
↓
5. AI
Deliver the information to:
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LLMs
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AI agents
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RAG systems
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Analytics
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Intelligent automation
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Business applications
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Decision-support systems
Why This Matters to Information Management
For years, capture was often treated as a relatively low-level technical function:
Get the paper into the system.
AI makes that approach inadequate.
Now we need to ask:
What information are we actually creating at the point of capture?
Is the image good enough?
Is it represented in a standardized and portable format?
Can it be preserved?
Can we establish its provenance?
Can we transform it into a representation that AI can efficiently consume?
Those are information-management questions.
And they are increasingly AI questions.
This is where the AIIM community has an important role to play.
Information professionals have spent decades dealing with:
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Information quality
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Metadata
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Document formats
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Records
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Retention
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Preservation
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Governance
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Interoperability
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Information lifecycle
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Business processes
AI doesn't make those disciplines obsolete.
It makes them more important.
Capture-for-AI Is an Information Pipeline
The most important concept may be that Capture-for-AI is not a single product.
It is a pipeline.
Physical document
↓
TWAIN Direct
Capture the image
↓
PDF/R
Standardized file for storage, transport, and exchange
↓
C2PA
Authenticity and provenance
↓
DocLang
AI-oriented transformation
↓
AI / LLM / Agent
Understanding and reasoning
↓
Business Process
Action
Each layer has a different responsibility.
That separation is important because it means organizations can improve individual parts of the pipeline without having to replace everything else.
A better capture device can improve the image.
A standardized file format can improve transportability and interoperability.
Provenance can improve trust.
An AI-oriented representation can improve downstream consumption.
And AI models can continue to evolve independently.
The Next Step: Let's Get Hands-On
There is a lot of discussion about AI.
There is considerably less discussion about the engineering that has to happen before the information reaches the AI model.
That's where I think Capture-for-AI gets interesting.
Rather than simply discussing the concept, we could actually build the pipeline and examine what happens at each stage.
What happens when we capture the same document at different resolutions?
How does image quality affect the information available downstream?
What does a standardized PDF/R file actually contain?
What metadata can travel with the document?
How can C2PA establish provenance?
What happens when that document is transformed into DocLang?
What information is preserved?
What information is transformed?
And ultimately:
How much better can we make the information before we give it to AI?
Would a technical, hands-on Capture-for-AI workshop be of interest to the AIIM Community?
A workshop could take a real document and walk through the complete pipeline—from image capture with TWAIN Direct, to PDF/R, to C2PA provenance, to DocLang transformation, and finally into an AI application.
Rather than simply talking about the technologies, we could build the pipeline, examine the outputs at each stage, and discuss the engineering and information-management decisions involved.
Would that kind of technical, hands-on workshop be of interest to you?
References and footnotes
[1] TWAIN Direct. The TWAIN Working Group's TWAIN Direct materials describe the technology and its role in network-oriented image acquisition.
TWAIN Direct
[2] PDF/R — ISO 23504. PDF/R-1 is formally ISO 23504-1:2020, Document management applications — Raster image transport and storage — Part 1: Use of ISO 32000 (PDF/R-1). ISO describes it as a subset of ISO 32000 for the storage, transport, and exchange of multi-page raster-image documents. (ISO)
ISO 23504-1:2020
[3] PDF/R background. The PDF Association describes PDF/Raster as a strict subset of PDF specifically intended for storing, transporting, and exchanging multi-page raster-image documents. It notes that PDF/R combines PDF's portability with the core functionality needed for raster capture and can serve as an alternative to TIFF and JPEG for scanner output. (PDF Association)
PDF/Raster 1.0 — PDF Association
[4] PDF standards. The PDF Association identifies PDF/R as ISO 23504 and distinguishes it from other specialized PDF standards, including PDF/A. (PDF Association)
PDF standards — PDF Association
[5] AI and PDF. The PDF Association's FAQ: AI and PDF discusses how PDF metadata and semantic information can be valuable to AI systems. It specifically notes that XMP metadata can communicate document-level and object-level information, while Tagged PDF can preserve logical reading order and semantic structures such as tables. (PDF Association)
FAQ: AI and PDF — PDF Association
[6] C2PA. The C2PA specifications define technical standards for certifying the source and history/provenance of media content. (C2PA)
C2PA Specifications
[7] DocLang. ABBYY describes DocLang as an open document standard for representing document structure, meaning, and layout in a machine-readable format optimized for AI consumption. (ABBYY)
ABBYY DocLang
[8] Important distinction on archival preservation. PDF/R's ISO scope is storage, transport, and exchange of raster-image documents. It should not be described as synonymous with PDF/A. PDF/A is the PDF family specifically developed for long-term preservation. In a production Capture-for-AI architecture, the appropriate preservation strategy should therefore be determined separately from the capture/transport format. (ISO)