The document discusses the three types of documents for intelligent document processing (IDP): structured, semi-structured, and unstructured, highlighting the complexities involved in processing each type. Structured documents have a fixed format, whereas semi-structured documents vary in layout but contain a consistent set of data, and unstructured documents lack any fixed layout. Effective processing of semi-structured and unstructured documents often requires advanced machine learning algorithms and significant customization, with many businesses opting for systems integrators for implementation, which can be time-consuming and may lead to accuracy issues.

1. Document Types Explained:
Structured, Semi-Structured,
and Unstructured
When you start looking for an intelligent document processing
(IDP) solution for your business, one of the first questions that
vendors ask you is what kind of documents do you have? They
expect you to give an answer from one of the three choices -
structured, unstructured, or semi-structured. But there is not one
definitive answer as to what kind of documents fall into which
category. Let’s take a closer look..
Structured Data vs. Unstructured Data
Before we start talking about documents, it would be worthwhile
to talk about where this conversation has come from.
Historically, transactional systems stored and processed data
that lived in databases. Most of this data has a clear structure -

2. each data element has a type, a defined length, and in some
cases, possible values. Previously, this data used to live in
cleanly structured tables as rows and columns within a database.
This is how this data looked:
Over time, systems started dealing with long, textual data which
was made of long strings of typed characters. This was slowly
complemented with images, videos, spreadsheets, audio files,
and all other sorts of multimedia content. This data was
collectively referred to as unstructured data because it did not
have any fixed format.
When you look at documents from this lens, all documents
collectively can be categorized into the unstructured data
category. This is the first point of confusion - unstructured data

3. and structured data do not map to structured documents and
unstructured documents.
All documents are unstructured data! But within these
documents, you can further classify them into three categories
based on how they appear:
1. Structured Documents
2. Semi-Structured Documents
3. Unstructured Documents
Structured Documents
These are the documents that have a fixed format, much like their
structured data cousins. You would usually see these as forms,
payment slips, or utility bills from a provider. As long as you deal
with just one provider, you’re dealing with structured documents.
The data in these documents have fixed locations - the date will
always be located in one place, the name of the person will
occupy a fixed location, etc.

4. Here is an example of how a structured document looks:
The technologies that can help you with extracting data from
these documents are fairly straightforward. You can put a
template that uses OCR and then goes to a specific coordinate
on the document to pull out values for different fields.
Important considerations
One big challenge with structured documents is that you need to
create one template for each of the providers. If you are
processing utility bills, you will need to create a template for
each different variation of the bill. This does not pose much of a

5. problem in the beginning when the number of variations is fewer.
But as variations increase, it becomes more than a full-time job
to keep creating templates for every new provider.
The second problem is that templates change. The providers may
redesign the layout of the document or upgrade their
document-producing software and inadvertently start sending
completely new document formats that break the template.
Unfortunately, you only find out that the template has changed
when your data extraction stops working. Then you need to work
overtime to fix the template and get it to work again.
Semi-Structured Documents
Some documents have a fixed set of data but no fixed format for
this data. In some documents, the date appears on the top right
corner, in another variation, it is at the center of the document,
and in yet another, you’ll find it in the bottom left corner. Another
added complication is that the same data is qualified by different
names. In one variation, a field may be called ‘Purchase Order
Number’, in another - ‘PO Number’, and a few others may call it
“PO #”, “PO No.” or “Order Number’. These variations are
endless and because of these two challenges, you cannot use a
template-based solution for these documents.
Data extraction from these documents needs robust machine
learning algorithms that can learn on their own. You will also

6. need some natural language processing capabilities to
understand the context of each field.
This is how semi-structured documents look:
As you can see, these documents essentially have the same
information but it is captured in a totally different format.
Important considerations
Processing semi-structured documents requires a probabilistic
approach based on machine learning algorithms. Without that,
you will get good results for a few document types and
not-so-great results for a long tail of variations. You will also
need capabilities to add new data points on the fly.

7. Unstructured Documents
The third category of documents is reserved for documents that
do not have any fixed layout or fixed data points. These are
free-flowing verbose documents similar to this blog post that can
have any information presented anywhere or in any format.
Data processing for these kinds of documents requires a
significant amount of configuration and customization to let the
IDP platform learn from your specific documents. This would
involve machine learning training, custom preprocessing

8. pipeline, computer vision-based recognition for visual
components such as charts, complex tables, and graphs.
Important considerations
Processing unstructured documents requires quite a bit of
investment upfront. It would be prudent to calculate the ROI for
these implementations before you go too far. You either need a
considerable volume of documents or business value for
unstructured documents. Second, since this implementation
involves quite a bit of customization, the time-to-market generally
takes more time. You can spend anywhere from 6 months to a
year to implement this type of solution. The key to success is to
split this problem into multiple phases and have measurable
success criteria for each phase.
In Summary
A majority of high-value documents are either semi-structured or
unstructured. OCR and manual corrections usually provide a
good enough return for simple, structured document processing.
However, more unstructured data needs very comprehensive
technology capabilities to process. There are a number of
vendors and solutions available for structured documents that
do a pretty good job of data extraction. But as you move into
semi-structured and unstructured documents, the vendor
landscape shrinks considerably.

9. The complications of variations that need template-free
extraction make it difficult for most IDP platforms to perform.
Most businesses are left with the only option of engaging a
Systems Integrator (SI) to custom implement these solutions.
These usually take a very long time to implement and often fail to
deliver on accuracy and speed. A comprehensive, machine
learning and AI-based IDP platform such as Infrrd can provide
you with the predictability and high accuracy needed in data
extraction for semi-structured and unstructured documents.