As a Solution Architect, I'm
often thinking about all my inputs
and outputs that must
occur within my architecture.
But what if that input
is actual paper documents?
Hello, my name is Jamil Spain,
Developer Advocate with IBM Cloud.
And for all those paper documents
that you have, you have to
think of a mass way that you want to
process all those.
My topic for the day is
automation document processing.
So let's take a look at what really
this concerns here.
It's really centered around one
major principle here is that you
want to get all the data, input
it from these forms.
And a lot of traditional workflows
actually encapsulate or having
or have that process of having
paper that needs to be filled out,
scanned in and turned in.
And what's your major option
to process that human people,
human workers actually have to
read and input that data entry
into the system here?
But the difference here with this
document processing is that
we're going to apply a
AI machine learning.
And a local, no
code solution to
allow you to build machine
models that actually learn from
a lot of these, the
context of what you're, the
documents that you have coming into
the system and allow you to build
some workflow that interprets and
what happens next once you have have
extracted all the data out from
that. So what are the benefits of
doing this? Why would I want to
invest the time to do this?
Well, I just mentioned one earlier.
With all these documents, you must
have manual people that manually
look at and process
this data. And after hours
and hours of looking at the same
document, we want to also
ensure, well, one thing that we
going to ensure by doing it from an
automated perspective is making
sure we have that data
accuracy.
And for any any way we'll
kind of replace or optimize
is take your people's time to do
other tasks because and eliminate
all the errors
that can occur from
people just looking at the same
document and mundane tasks of
interpreting all this information
here. So, let's
bring a little more perspective to
this. So now that we've kind of
set down the context of what we're
discussing, let's get to the
whiteboard over here to the right
and kind of figure out, or the
lightboard over here, and figure out
how does this kind of map out here?
Well, we know that, let me see if I
can do a person here.
They have a document that they're
going to always put in, and these
could be multiples of single or
we're talking to the thousands and
thousands. Let's think of it as a
doctor's office that has to process
all this paper that come in.
Haven't quite digitized their
process yet, but this is one way
they can still do their traditional
business and make that input
of data that they want to get off
those forms to be
digitized that workflow.
So when you have these actual
documents that are coming in, what
are you going to actually do is,
I'm going to abbreviate here the
document processing cloud, let's
say.
So that data is going to be coming
in. Now, inside here we're going to
make sure that we make a model
that really studies the particular
information that's there.
We may be able to classify some
fields as personal data,
PII data, personal information
or public information or information
that's protected, so we have that
ability there.
And it's major job
as that document comes in to making
a model, it's going to try to
interpret. We can highlight the
fields and must be there to say, you
need to pull this information off
the page. All right.
It would try its best to kind of
interpret that as well.
And I can also tell it to if
information does not is not clear
or it cannot interpret it outright.
Let that be a state that user
intervention is required where I
must say, "I agree this
is what I it think it has found.
This is what I agree that it found."
But the important thing here is the
machine learning.
So as we start processing more
and more documents, it gets the
learn from all the use cases
that it comes across and get smarter
and smarter, get more and more
efficient from there.
But as you kind of go in,
the output of that is going to be
the data that you want to extract.
And so whether that integrates into
other systems through APIs
or some type of other application
that needs to data.
You can now start to go in and
continue processing and go
from there as well.
So what we discovered today
is document processing,
having large amounts of paper that
you want to digitize into
and get that workflow to be
something you can speed up
and really meet the demand
that processing needs to occur
today in our common event
driven applications that we kind of
go into.
So this is a great discipline to get
into for your project if you
have these symptoms of documents
that you want to process.
I recommend you give document
processing a look.
Thank you for your time.
If you have any questions, please
drop us a line below, and
if you want to see more videos like
this in the future, please
like and subscribe.