The document addresses frequently asked questions about Intelligent Document Processing (IDP) technologies, highlighting their capabilities, deployment options, and processing technologies. It emphasizes the importance of understanding how IDP solutions return extracted data, whether for human or system consumption, and discusses the criteria for choosing between cloud and on-premise solutions. Additionally, it details the types of documents IDP can handle, the training process for machine learning models, and the need for customization based on specific business requirements.

1. Frequently Asked Questions
About IDP
Intelligent Document Processing (IDP) is a relatively new
segment of technologies aimed at intelligent automation. As we
talk to prospects and answer their questions about IDP, we find
some repetitive themes. In this post, we will look at some of the
most frequently asked questions about IDP and answer them
with our perspectives.
Let’s begin.

2. Q: Are IDP solutions API-based? Should they be API-based?
What does it mean when an IDP solution is API-based or
supports API output?
A: The way most IDP platforms work is that they receive a
document as input, do their magic, and extract important
business information from these documents. This information
extracted at the end can be returned to the business systems in
two broad ways - one for consumption by a human being and the
second for consumption by another system.
For human consumption, IDP systems display this information in
a user interface where the customer can verify the information
and correct it if needed. This information can also be returned via
a file exchanged over email or shared storage options like
Google Drive, Dropbox, etc. That is the second means of
returning this information.
For consumption by other systems instead of human beings, this
information is returned via an API or Application Programming
Interface. The API response can be readily consumed by other
technologies like an ERP system or an RPA bot. So when you
look at an IDP system, you should think about who is going to
consume this information eventually. If that consumer is a
system or a Bot, you will need an API response.

3. Q: Where are IDP solutions implemented? Cloud or On-Prem? If
the vendor has the capabilities for both, how should I analyze
what works best for my business?
A: Cloud and on-premise are the two main means of deploying
IDP solutions. There is a third, lesser-known solution,
desktop-based IDP applications. These applications are quite
light in nature, do not use extensive machine learning, and can
be installed independently on the user’s machine. More mature
IDP platforms that can process complex documents will need a
few servers - either on-premise or in the cloud. There are two
main criteria to help you choose the best solution for your
business:
1. Data Privacy: Due to the nature of the sensitive data that a
business handles, sometimes it is not an option to let that data
go out of the business’ control. In that case, businesses prefer
an on-premise solution where they keep the data locally. The
downside is that the on-premise solutions are costlier, do not
always get the latest algorithmic enhancements, and need
technical support staff to support it.
2. Cost: Since the cloud-hosted option is shared, it costs less.
You also save costs by not investing in your own infrastructure
or the technical support team. The downside is that your data will
leave your system and be processed in the cloud.

4. Q: Does an IDP solution use OCR? Do they use proprietary OCR
or do they have OCR Partners? As the OCR determines the
quality of extraction, how do I get insights as a customer to the
OCR engine capabilities of IDP vendors?
A: This is a great question. IDP solutions roughly use 5
technologies: Computer Vision, Predictive Analytics, Natural
Language Processing, Machine Learning, and OCR. Most IDP
solutions use multiple OCR engines. Some of them have their
own OCR engine, some use third-party OCRs. Most IDP platform
vendors will tell you what they use. But as a customer, you
should focus on OCR performance rather than worrying about
which OCR engine is being used. The reason is that even if an
IDP platform uses third-party OCR, it might have done significant
pre-and post-processing on OCR input and output. So, you will
get a more accurate extraction compared to using that OCR
engine directly.
Try your complex, low-quality documents on different IDP
platforms and see which one handles your documents better.
You should go with a better solution rather than a specific OCR
engine.

5. Q: I did my research and did not find a perfect solution that
meets my needs. Why is there not a perfect AI solution for IDP
extraction?
A: There are two sides to this coin. Most IDP solutions excel at a
particular type of document - structured, semi-structured, or
unstructured. But on the other hand, businesses do not have just
one type of document. Every business generally has a
combination of all three types of documents. That is why most
businesses find themselves in a situation where they pick a
really good solution for forms, but then realize that it cannot
handle semi-structured documents as well. It needs
customization to handle these documents.
As we explained in our recent blog post regarding document
types, the broader systems are easier to configure. If you find a
system that can handle unstructured documents, it is not
configured to handle structured documents like forms. But the
reverse is not true. So, you should figure out what is the
broadest category of documents that your business needs to
process. If it is semi-structured documents, then pick an IDP
platform that excels at semi-structured documents and configure
it for other documents.

6. Q: I have a document, say an insurance document from which I
want to extract printed demographic information and some
key-value pairs from the handwritten text (not just detection but
free form writing, such as a doctor’s prescription),. I have
another document with clinical information that may be a single
one with information about multiple patients. Would IDP be a
good choice for me?
A: Absolutely!
In fact, true IDP solutions are full platforms rather than just
products. Their strength is in not solving one problem really well
but rather handling all aspects of document data extraction. You
can design a specific solution and workflow based on this
platform that best suits your business. A comprehensive IDP
solution needs some level of configuration along with
out-of-the-box capabilities.
Q: What is the process for retraining? Are corrections captured
and added to the ML learning cycle?
A: Machine learning systems are in some ways like Chinese
bamboo trees. Just like with these trees you need to invest time
upfront training a system on your specific documents. It tries to
learn variations, understand layouts, and build a foundational

7. capability to support the prediction wave that is about to come.
But once it takes off, it gives you amazing accuracy.
Here is reference data from an actual live implementation of
Infrrd’s IDP platform for a document that had thousands of
variations. It was near impossible to create and manage
templates for this type of document that did not follow a fixed
format, and data was never in a fixed position on the document.
This is how the accuracy improved with each iteration of training:
By the time the customer started using the final version of the
model, it had a very high accuracy rate.

8. While other traditional systems provided a 65% accuracy rate
against the initial accuracy of 63% of this model, in a few weeks,
it outperformed every other accuracy benchmark that the
customer had experienced. It feeds off of the corrections made
by the data processing teams as well as the new document
layouts and variations that it had not processed before. When the
system processes a document and even when the results are not
corrected, it learns that it has performed the right extraction. This
increase in accuracy is made possible by continuous relearning
using our feedback loop.

9. Q: What is the average number of documents/training
time/training set data required for a model to be effective? What
is Infrrd’s process?
A: The answer to this question largely depends on the type of
documents. For some documents, one month of data is enough
to learn to get things started. For more complex documents, it
may take 3 months of data. Infrrd’s approach is to get the
customer started with what they have. How many documents you
have isn’t a critical issue. The important thing is that every day or
every week as you process documents, you see your accuracy
numbers climbing.
You can start with zero documents on day one if you want.
Though this will mean you will also start with zero % accuracy.
This is not such a bad thing for customers who process all of
their data manually. But they will see the manual effort going
down every week and see the accuracy climbing.
Q: What can IDP models process out of the box and what
configuration is possible? Can IDP models only process PDF
documents? Can these models process data in an email
message combined with information in a document? What about
things like logos, symbols, and signatures?

10. A: Different types of IDP solutions can process different kinds of
data. Infrrd’s IDP platform excels at processing semi and
unstructured data. We handle most of the semi-structured data
out of the box. Because of the broad nature of unstructured data,
your specific implementation may need some customization on
top of our platform.
Capabilities to handle visual data like tables, logos, signatures,
and symbols are built into our platform. The unique value
proposition of our platform is its ability to learn from each and
every customer’s data. Our visual recognition engine can predict
logos for one customer and predict shipping handling symbols
for another.