>> You're not going to want to miss
this episode of The AI Show we talk all about
automating data extraction from
ID type documents using Azure Form Recognizer.
Make sure you tune in.
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>> Hello.
Welcome to this episode of the AI Show.
We're talking all about automating data extraction
from ID-type documents using Azure Form Recognizer.
We've got a special guest. How are you doing
my friend? Tell us who you are and what you do.
>> Hi Seth. Hello everyone.
My name is Lu Zhang.
I'm a Program Manager working on Azure Form Recognizer.
>> Fantastic. Let's start with Form Recognizer in general.
What is that and how can it be helpful to us?
>> Form Recognizer is a AI power to document
extractions service which can
help you extract the key-value pairs,
the tables, and the structure from
your document using deep learning models.
>> That's fantastic. Are people using it?
Are people excited about it?
>> Yes, exactly.
It's a keypad for your RPA,
the Robotic Process Automation,
so a lot of document needs to be processed,
and the Form Recognizer is a perfect fit for that.
>> Let's start with the specific.
You're talking about ID documents today.
Can you tell us a little about what
Form Recognizer does for those documents?
>> Sure, yeah. Generally,
Form Recognizer has three big buckets for the offerings.
You can use layout to extract the tables,
you can use the pre-built for invoices,
for receipts, and for ID document
to extract the key-value pairs
and line items from these common documents.
You can also use just five samples to
build your custom model to extract the key information.
What I'm going to focus on today is basically the IDs pre-built.
>> Now, what is an ID document?
I think we need to clarify what that is,
so people know what this is doing.
>> Sure. Basically,
ID documents can cover a wide range of different types.
It can be a passport,
it can be driver's license,
it can be other ID cards,
but these ID document are
the key information in the know-your-customers scenario.
But these different types,
if you want to extract by yourself,
that will be very challenging, and of course,
because the information are very sensitive on the ID documents,
so you need to meet the compliance and security requirements,
which make it also challenging
for different companies to do it on their own.
>> If I'm understanding this right,
this is for recognizing data on an ID.
If someone wants to identify themselves with an ID,
it's basically a picture of
an ID-type document that
then you recognize. Am I getting this right?
>> Yes, exactly.
>> Awesome. Can you show us how it works?
>> Sure. Let me show a quick demo.
This is our FOTT,
the UX portal which you can easily try out our IDs Pre-built.
You can just go to the puddle and then upload image,
for example, the passport from the United States.
What you just need to do is once
you have the Form Recognizer results,
just copy the endpoint information and
the key information here and select ID.
Just click the button,
and you should be able to extract
all this information like the country region,
the date of birth,
and the first name,
last name, and the gender information from the IDs document.
>> This is really cool. Now, this program that you're using,
FOTT, what is this program?
>> This is the UX demo tool.
Basically, you can try out our service very
easily without writing a single line of code and you
can get to see the results by
downloading the JSON output and the CSV from the portal as well.
I'm going to show how the JSON looks like in later section.
>> This program that you're looking at,
so this is the software that anybody can download and use.
Is it like an open-source thing?
>> It's a open source.
We have GitHub repo that you can download all the source code.
>> Okay. That's cool. Basically,
this application that you're using is showing off how
to use ID document recognition,
but then you can download this and
modify it for whatever that you want.
>> Yeah, exactly.
>> Fantastic. Here's the next question.
Let's just say I want to write my own backend or I want to use
my own code to actually
do the type of recognition that you are showing.
Is that possible as well?
>> Yeah. That's also possible.
What I'm going to show next is Jupiter Notebook,
how you can leverage our Python SDK and the
rest API to recognize from the ID document.
>> All right, let's take a look.
>> Basically, what you need to do,
this is a Python Notebook and you need
to pip install the package,
the Azure AI Form Recognizer.
Here I have already installed.
What you need to do next is to set
up the endpoint then key information,
which you will be calling our Form Recognizer Service.
Next, what you can do is to analyze Form Recognizer client.
Here I have
my sample driver's license image which you can see here.
This client will begin recognize
the ID document by fitting the contents of the image.
Here we see it's succeeded just within a few seconds.
What you can also do is we support
you just a fitting too with
the URL of the image directly to start to analyze.
It should also be very quick to complete the analysis.
After that, we are going to show what fields can
be extracted using this functionality.
As you can see, the result is
returned and we can just output the result.
Basically, we output for each of
the field like the address, the country, region,
date of birth, with the actual value
we passed and together with the confidence score.
>> That's really cool. Basically,
you can use this for
any kind of application inside of your organization. Is that right?
>> Yeah, exactly. For this client ID case,
we not only support Python,
we also support Java,
C Sharp Java scripts so that you can embed into your application.
>> That is amazing. What else are you showing us here?
>> The other example I'm going to show
is we also offer the rest API,
which you can simply put
a post request and send to this URL based on
your Form Recognizer results and you can also
send your file contents there using the postal request.
After that, you can use the get request to get the JSON output.
Here I got successfully analyzed
the results and we can look into the JSON output a little bit.
Another thing is we have the sample code which you
can output to a CSV file as well.
>> That's cool. I like this.
>> This is another like I play around
this output and you see from the JSON output,
we can look into it a little bit.
We have the real results.
This is basically the OCR result with
all the tags fields together with
the bounding box recognized from the image.
We also have the document results.
This is the key-value pair we are just showing,
for each of the key,
like the address, the actual value we passed,
and the bounding box from the image and the confidence calm.
>> This is cool. If I'm understanding it right, basically,
this API allows you to do everything
that the other demo that you showed us,
but you're basically calling the endpoints.
I thought I saw two different requests.
The first request was it sends it and brings it back,
and there was another one that starts the request and then you
pull to get an answer back if it's a larger set.
Can you submit a bunch of these at the same time?
>> Yes. You can submit a bunch of the documents,
and each document can have multiple pages.
You can just send it to our API together without.
>> Got it. If someone's watching this and they're thinking,
I'm not a coder and I don't want to use this application,
the other application because I'm not going to
download it from GitHub and set it up,
but it feels like
you can automate this a lot more because basically,
you might have a lot of pictures somewhere and
you just want to run it against this picture.
Is there a way to do this in a low code, no code way?
>> Yes, we have that offering embedded into the AI Builder.
Basically, you can build your own workflow using
the Power Automate or Power Apps and we have
all the building blocks which you
can find from the AI Builders page.
You can easily start from here.
>> As this kid is getting loaded,
can you explain what the AI Builder actually is?
>> AI Builder is actually a low code,
no code way for these citizen developers.
They can build their own automated flow applications.
>> Let's see what we've got here.
>> You see we have all these Pre-built
Business Card Reader, ID reader.
You can also process the invoice,
you can do the keyphrase extraction.
These are all the embedded AI capabilities that you
can use in your automated flow application.
>> That's cool. How would you use
the ID flow that you've been showing us?
>> Basically, you can start a new flow from here.
I have already built one flow,
which we can see a little bit.
Basically, it's a simple flow,
you manually trigger by upload the file content.
This is the AI Builder module.
You can call this the ID Reader Module to extract the information.
The last step is just to extract
the first name and the last name,
insert a new row into my Excel spreadsheet.
>> That's awesome. Are we going to run it?
>> Yes. We can do a quick test on that.
Let's start testing.
Let's manually upload a document.
Let's upload this one and just around the flow.
Okay, it's done. We can see
there should be a new row inserted into my Excel spreadsheet.
You see, that's a new row.
>> That's so cool.
Well, this is all pretty cool stuff.
Where can people go to find out more
or get started with these cool tools?
>> You can go to our documentation site for Azure Form Recognizer.
This is the homepage and explore the different capabilities here.
If you want to learn more about IDs,
this is the concept page.
You will be able to try it out,
you will be able to learn without the fields extracted
and all the additional requirements.
>> Fantastic. Well, Lu,
it's always a pleasure being with you.
Anything else you want to say before we go?
>> Just try out
our Form Recognizer Service and
let us know if you have any feedback.
>> Awesome. Thank you so much for being with us, Lu.
Thank you so much for watching and learning all about automating
data extraction from ID-type documents with Azure Form Recognizer.
Thank you so much for watching
and hopefully we'll see you next time.
Take care.
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