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ANU SRIVASTAVA: Hi, everyone.
My name is Anu Srivastava, and I'm
excited to talk to you today about the new Cloud Document AI
platform.
Let's dive in to learn more.
Document AI is Google's solution for understanding documents
in what we consider dark formats,
such as PDFs, handwritten forms, and images.
Using the power of AI, Document AI
turns these unstructured documents
into structured, actionable data.
For example, you could process multipage handwritten
applications with a form parser.
There are also several specialized parsers already
created, so you could be batch processing tax documents
by this afternoon.
With structured data, you can fuel better business decisions,
ensure that your data is accurate and compliant,
and use your data to meet customer expectations.
Or hey, maybe you're just sitting
on a pile of crumpled receipts you want to digitize.
It's easy to get started with our specialized parsers,
or bring your own document type and test out our form parser.
It's all in the new Document AI platform.
Let's take a look.
The platform, available in the Google Cloud Console,
is where you can create, modify, and test out your processor.
We'll walk through an example on the platform
and talk about what's going on under the hood
to show you what makes Document AI special.
Today, we'll demo how to parse a driver's license application
and show you how to integrate it into your app or service.
Here is an image on my form.
You can see it's been damaged, it's crumpled, and just
doesn't have great image quality.
I want to test out what the generic form parser will
be able to extract, so I will create an instance of it here.
I intend to use it in my application's code on more
documents, so these variables up here will be important later.
Let's test it out.
You can see the key-value pairs extracted.
They're now in a digital format, easy to use.
So what happens under the hood when you run a document
through a processor?
First, Doc Prep scans, cleans, and extracts text
from the image using Google OCR, which
supports over 150 languages.
Next, the words, text, and visual elements
are used for classification when required.
Google uses an ensemble of natural-language models
to extract simple and compound entities.
When the entities are related, they're grouped together.
Last but not least, your document
is packaged up in a unified document format
and sent back to you.
Now that we understand what's going on,
let's integrate my processor into my application.
I'm showing some Python code today,
but we have client libraries available in several languages.
This API is incredibly simple for how much
is happening behind the scenes.
All I need is the name of my processor,
which I can construct with the variables from the Console,
and then just the bytes of the document or the image.
Voila.
Now I can expect all the spatial and text data back
in an easy-to-use JSON format.
Also, with all of these key-value pairs,
it's easy to integrate this data in other services, storage,
or visualization tools I might be using.
We're excited to see what you can do
with the power of Document AI.
Get started today, and let us know
what use cases you're solving.
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