Session 4 AI Associate Series: UiPath Document Understanding Overview

🚀 Welcome to Session 4/ AI Associate Developer Series 2024!
In this session, we will explore the concepts and capabilities of UiPath Document Understanding.

📕 Agenda:
Introductions
The stages of the Document Understanding framework
The main Document Understanding components
The Document Understanding project lifecycle
Document Understanding licensing

👨‍🏫 Speakers & Mentors:
Marcelo Cruz, UiPath MVP, RPA Teacher and Consultant
Sean Jerome Llanto, UiPath MVP, Solutions Architect @Ingram Micro
Srinivas Kadamati. UiPath MVP, Hyper-Automation Sr. Tech Lead @Prosevartner
Mudhukrishna Lalapet, UiPath MVP, Technical Lead @L&T Technology Services

👉 📆 Register here for upcoming Session 5: Build a Document Understanding Automation in Studio: https://bit.ly/AI_Associate_Session5_2024

1. UiPath Document
Understanding

2. 2
UiPath MVP
RPA Teacher and Consultant
UiPath MVP
Solutions Architect
Ingram Micro
UiPath MVP
Senior Tech Lead
Proservartner
Marcelo Cruz Sean Jerome Llanto Srinivas K
Team Slide

3. 3
Get your documents
processed intelligently
Teach your robots to understand documents
using AI-enhanced skillsfor data extraction
and interpretation.
Drag and drop these capabilities directly into
your automationworkflows to embed AI

4. 4
What is document understanding?
Document
Understanding
Artificial
Intelligence
(AI)
Document
Processing
Robotic Process
Automation
(RPA)
Document understanding is the ability to
extractand interpret information and
meaningfrom a wide range of documents.
It emerges at the intersection of document
processing, AI, and RPA.
Not OCR
Not Computer Vision

5. 5
© Copyright UiPath 2022. All Rights Reserved.
Document Understanding Features
Document Understanding offers end-to-end capabilities to use a combination of rule-based and model-based approaches to
process documents.
Composable framework for
flexibility and best
technological choices
End-to-end solution for
extracting and interpreting
information
Support for various types of
documents
Support for processing
different file formats
Recognition of various
document objects
Usage of templates/rules and
Machine Learning (ML)
models to understand data
AI-powered capabilities Model retraining capabilitiesAvailability in cloud and on-
premise

6. 6
Teach robots how to process your documents using intelligent
drag-and-drop skills for dataextraction and interpretation​
IN
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AI understands documents, takes actions,
and learns from the data
Getting rid of the “noise” caused by
unrelated, rotated, or skewed documents
Saving time and costs with seamless
end-to-end automation
Processing a wide range of documents and
layouts, handwriting, checkboxes
Machine learning (ML) skills improve over
time based on the custom data
Mix of template and template-less
approaches for most accurate results

7. 7
•Like forms, passports, licenses, time
sheets
•Fixed in format and can contain
handwriting, signatures, checkboxes​
•Like invoices, receipts, purchase
orders, medical bills, utility bills
•Containing fixedand variable parts
like tables
•Like contracts,agreements, emails,
scripts, drug prescriptions, news
•No fixed format, free-form
sentences/paragraphs
Which documents can be handled by
Document Understanding?
Structured documents Semi-structured documents Unstructured documents

8. UiPath Document
Understanding Overview

9. 9
© Copyright UiPath 2022. All Rights Reserved.
Document Understanding and the
UiPath Business Automation Platform
Studio
Robots
Orchestrator
Action Center
Pre-trained models available out of
the box
Bring your own model -custom or
third-party
Retrain the models
Core RPA tools
Human validation
Integration
Service
AI Center

10. 10
1 2 3
Understand ActReceive
Document Types
•Structured
•Semi-structured
•Unstructured
Document Variety
•Multiple languages
•Various formats
•Varying templates
•Handwriting
•Signatures
•Skewed docs
•Checkboxes
•Low quality scans
Streamline end-to-end processes,
improve business outcomes
and reduce manual effort
Built for: RPA and Citizen Developers –no data science skills required
Built-in ML: Pre-trained ML models, data labeling, retraining
Built as: SaaS, Self-hosted or Air-gapped integrated with UiPath Business Automation Platform
DIGITIZE
CLASSIFY
EXTRACT
VALIDATE
RETRAIN
ANALYZE
End-to-end Intelligent Document
Processing solution

11. 11
Load taxonomy for
your documents
How Document Understanding works
Digitizeimages using
multiple OCRs
Classify documents Extract named entities
in a taxonomy
Validate and train
supervised models
Exportextracted data
2 3 4 5 6
Digitize Classify
Train & Validate
Extract
2 3
5
4
Structured
Semi-structured
Unstructured
Export
RPA
BPM
API
Other systems
6
1
Define taxonomy –
document types and fields
1

12. 12
Document Understanding Typical Workflow
Load taxonomy
defines document
types and fields for
processing.
Digitize
documents using
Optical Character
Recognition (OCR) to
make them machine-
readable.
Classify
and split the files into
document types.
Extract
information from the
documents.
Export
the extracted data for
further usage.
Train
classifiers based on
the validated data.
Train
extractors based on
the validated data.
Validate
classification results
(human review).
Validate
extractors results
(human review).

13. UiPath Document
Understanding Overview
-A Closer Look

14. 14
Taxonomy manager is
used once at the start to
define the collection of
documents that you
would want to process
as well as business rules.
Additionally, you can
describe what datayou
would like to extract.
Load taxonomy

15. 15
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We move so you don’t need to move
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1 February 2020
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20800 ALMADEN AVE, SUITE 404
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T: [phone]
F: [phone]
E: [email protected]
www.moveforyou-co.com
Bill To:
Tony Tzeng
12345 Mango Lane
Seattle, WA 98108
INVOICE DETAILS
Packing services
Storage fees (1 month)
House move (white-glove service)
Vehicle storage and transport
Sales tax 10%
Total Fee including Tax
FEE
$1,282.00
$1,884.00
$5,320.00
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$1,367.20
$15,039.20
Methods of payment
Personal Check: Move-For-You LLC
Wire Transfer: BigBankCo., Account 123456789-0987ABC
Invoice No: 456200-TZE1
Digitize text in the documents
using OCR

16. 16
Classifyand split the documents
Documents scanned into one file
isn’t a problem –owing to
classifiers, the robot can identify
the document types and split the file
to process the documents
accordingly.
Document Understanding offers
different classification capabilities
ranging from keyword-based to
ML-based classification.

17. 17
Validate classification of the
documents
Classification Station is
used to check, correct, and
confirm the results of
document classification and
splitting.

18. 18
You can easily configure
data extraction to choose
most suitable extractor
for each field.
Use a combination of rule-
based and model-based
approaches to ensure
smooth and accurate
processing of different
documents.
Extract data from the documents

19. 19
Validate Extraction of Documents
▪Validate the extracted
information and handle
exceptions using
Validation Station.
▪Now, retrain ML models
using the data confirmed
or corrected in Validation
Station.

20. 20
© Copyright UiPath 2022. All Rights Reserved.
Train Classifiers and Extractors
Let the classifiers and
extractors learn from the
data corrected and validated
in Classification Station and
Validation Station,
respectively.

21. 21
Export the Extracted Data
End-to-end intelligent
document processing
Start & continue the document
processing workflows with other
automation components.
Export the data for further
usage/automation, for example,
to an Excel spreadsheet, to SAP
system, send as an email, and so
on.
Start
Document Understanding
Decisions
Action
Action
Action
End

22. Document Processing
Methodologies

23. 23
DocumentTypes
▪Required information found in
the same place
▪Fixed in format
▪Examples: Forms, passports,
licenses, and time sheets
containing handwritten text,
signatures, checkboxes
▪Repetitive information each time
▪Found in fixed and variable
document parts such as tables
▪Examples: Invoices, receipts,
purchase orders, medical bills,
bank statements, utility bills
▪No fixed format
▪Examples:
Contracts,agreements,
emails,diseasedescriptions,
drug prescriptions, news, voice
scripts​
Structured Semi-structured Unstructured

24. 24
Document Processing Methodologies
Based on the document type, there are two common types of
data extraction methodologies namely, rule-based and model-
based.
▪Rule-based approaches require users to create
rules/templates that can best extract information from their
documents.
▪Model-based approaches rely on ML and statistical
techniques.
Both approaches are extremely potent tools but
sometimeslimited in their abilities to process optimally the range
ofdocuments companies can manage.
The Document Understandingframework overcomes these
limitations of an individual approach by implementing the hybrid
approach.
Hybrid
Rule-based Model-based

25. 25
Document Processing Methodologies
(Cont’d)
Rule-based
Structured fields, mostly
used for structured
documents
Mostly structured
documents, tables,
checkboxes,
handwriting, signatures
Most structured
documents (forms)
Mostly semi–structured
documents
RegEx Based
Extractor
Form Extractor Forms AI Machine Learning
Extractor
Model-based
Hybrid
A combination of both –rule-based and model-based extractors
Mostly documents combining both structured and less structured formats

26. 26
Document Processing Methodologies
(Cont’d)
Enables users to
create and use a
customized Regular
Expression (RegEx)
to extract
information from a
document.
Rule-based
Structured fields, mostly
used for structured
documents
Mostly structured
documents, tables,
checkboxes,
handwriting, signatures
Most structured
documents (forms)
Mostly semi–structured
documents
RegEx Based
Extractor
Form Extractor Forms AI Machine Learning
Extractor
Model-based
Hybrid
A combination of both –rule-based and model-based extractors
Mostly documents combining both structured and less structured formats

27. 27
Document Processing Methodologies
(Cont’d)
Enables users to
create templates to
extract, match, and
report information
by taking into
consideration the
words' position
inside the
document.
Rule-based
Structured fields, mostly
used for structured
documents
Mostly structured
documents, tables,
checkboxes,
handwriting, signatures
Most structured
documents (forms)
Mostly semi–structured
documents
RegEx Based
Extractor
Form Extractor Forms AI Machine Learning
Extractor
Model-based
Hybrid
A combination of both –rule-based and model-based extractors
Mostly documents combining both structured and less structured formats

28. 28
Document Processing Methodologies
(Cont’d)
Processes forms
and documents that
have similar
formats and fixed
formats with low
diversity in layouts
and provides point-
and-click usage
experience.
Rule-based
Structured fields, mostly
used for structured
documents
Mostly structured
documents, tables,
checkboxes,
handwriting, signatures
Most structured
documents (forms)
Mostly semi–structured
documents
RegEx Based
Extractor
Form Extractor Forms AI Machine Learning
Extractor
Model-based
Hybrid
A combination of both –rule-based and model-based extractors
Mostly documents combining both structured and less structured formats

29. 29
Document Processing Methodologies
(Cont’d)
Enables users to
extract template-less
similar data points
fromsemi-structured
orunstructured
documents using
MLmodels.​
Rule-based
Structured fields, mostly
used for structured
documents
Mostly structured
documents, tables,
checkboxes,
handwriting, signatures
Most structured
documents (forms)
Mostly semi–structured
documents
RegEx Based
Extractor
Form Extractor Forms AI Machine Learning
Extractor
Model-based
Hybrid
A combination of both –rule-based and model-based extractors
Mostly documents combining both structured and less structured formats

30. 30
Rule-based or template-based approach
Relies on rules (like regular
expressions) and templates
(including anchors)
Processes fixed in format
structured data
Ensures high accuracy for
already known documents

31. 31
Pre-trained models
Machine learning (ML) models
as a template-less approach
Custom models
•No-code light-weight models in Forms AI
•Custom ML models in AI Center
•Third-party models
Model retraining
Learn about sharing data for model retraining here
•Invoices
•Receipts
•Purchase Orders
•Utility Bills
•Passports
•ID Cards*
•Legal Contracts
•W-2 Forms
•W-9 Forms
•Delivery Notes
•Remittance
Advices
•ACORD 125
•I9 Forms
•990 Forms
•4506T Forms
•FM1003 Forms
•Pay slips & personal
earnings statements
•Certificates of origin
•EU declarations of
conformity
•Children’s product
certificates
•Certificates of
incorporation
•Shipping invoices
•CMS1500
•Retraining via AI Center
•Continuous learning loop based on human validated data

32. 32
Make use of pre-trained ML
modelsto process invoices,
receipts, utility bills, ID cards,and
many more document types.
Retrainthe models to optimize
them for your custom documents
and improve the model accuracy
over time!
Bring your own model or third
party models and incorporate
them in your automations.
Pre-trained ML models

33. 33
ML model training via AI Center
You can use Document
Manager to train your custom
ML models or retrain the
existing models in AI Center.
This would help robots
understand the specificities of
your documents better. The
more you work with the model,
the more effective it becomes.
Thus, the accuracy of the
extracted data improves over
time.
Learn about sharing data for model retraining here.

34. 34
Example scenario:
Mortgage packet audit post-closing
Extract key loan
information from
documents
Split the packet into
underlying files for
faster processing
Robot monitors folder
for new files, initiates
document process
Executed closing packet
received and scanned
•Document scanning
•Digitization with OCR
•Unattended robot •Pre-processing
•Document classification
(keyword, anchors, model)
•RPA parallelization
•Extraction
•ML model-based and/or
rule-based (hybrid)
1 2 3 4
Write results into line of
business application
Send exceptions for
human review
Compare / validate
information across
documents
Check for signature
present in executed fields
•Signature detection •Unattended robot •Confidence / business rule-
based exceptions
•Validation Station
•Attended robot
•Action Center (Unattended
RPA)
•Unattended robot
5 6 7 8

35. UiPath Document
Understanding Template
Demo

36. Annex

37. 37
IDP combines Document Understandingand Communications Miningcapabilities to help customers automate document
processing from end to end. It delivers state-of-the-art Specialized AI and Generative AI (Gen AI) for all scenarios -
documents and communications, structured, semi-structured and unstructured.
Intelligent Document Processing (IDP)
Introduction
Extracts relevant data from documents.
70+ pre-built models to analyze and
process different types of documents
across industries and domains.
Requests or emails with
attacheddocuments:
•Multiple languages
•Various formats
•Handwriting
•Signatures
•Skewed & low-quality scans
•Checkboxes
•Tables
Human in the loop
Asking employees to validate the
results if required or in case of
inaccuracies and exceptions.
UiPath Automation
Route the extracted actions and data
to downstream systems for further
processing.
Extracts key intent, sentiment and
context data from messages. The latest
advances in AI and machine learning
(ML).

38. 38
Latest GenAIenhancement in
our IDP offering
Generative
Extraction
General availability Now
Active
Learning
Public Preview Now,
General availability April
Generative
Annotation
General availability Now
Generative
Classification
General availability Now
Zero-Shot
Discovery
Public preview April
Generative
Extraction
General availability Now
Generative
Validation
Public preview Now,
General availability April
Autopilot
TM
for
CommunicationsMining
Public previewnow
Generative
Annotation
General availability Now
Active
Learning
General availability Now

39. 39
Document Understanding:
Generative Annotation (Pre-labeling)
What is it?
Fast & easy document annotation for ML
model training withGenerative AI
You can annotate any document samples
with Gen AI, accelerating annotation from a
week to a day or two for complex scenarios,
or down to minutes for simpler forms.

40. 40
Document Understanding: Generative
Classification
What is it?
Document classification made easy with
Generative AI
Classifying documents isfast and easywith
Gen AI –justdefine the document types, no
need to write rules or train new ML models.

41. 41
Document Understanding: Generative
Extraction
What is it?
Question-answering model powered by
Generative AI
Generative AI can answer questions and
summarize content which worksperfectly for
free-form unstructured documents –with no
need to train custom ML models.

42. 42
Document Understanding:
Generative Validation
What is it?
Get a ‘second opinion’ on the extracted
data from Generative AI to reduce the
human validation effort
With Generative AI used to confirm the output
of Specialized AI, the overall automation rate
increases by up to 200% and the average
handle time decreases –reducing the time
spent on human validation.
When will it be available?
•Public Preview now, GA in 2024.4
Source: Test by UiPath AI R&D on a diverse set of enterprise documents​
200%
increase
Automation Rate

43. 43
Next-generation Document Understanding
with active learning
What?
Active learning is a next-gen AI-
powered experience within UiPath
Document Understanding
Why?
•80% faster model training—from a
week, down to just a day
•Anyonecan train AI models—no coding
or ML skills required
•Guidance on model optimization—
humans & AI collaborating together
•Instant model evaluation—built-in model
performance analytics
Where and when?
Public Preview now, GA in 2024.4

44. 44
Statement 1 Statement 2 Statement 3
Three Statement Slide

45. 45
“Quote text goes here. It can be short,
but it shouldn’t be too long.”
Author Name goes here
Author Title goes here

46. 46
Computer
Vision
First Robotic
Automation
Early
Growth
Growth Global
Expansion
Category
Leader
First automation
libraries for
developers worldwide
Desktop Automation
product for
Enterprise RPA
Enterprise RPA
Partnerships with
global BPO &
Consulting Firms
Global offices
100 people
100+ enterprise
customers
Entered Japan
Launched Academy
Series A
700 Customers
550 People
100,000 Community
2,500 Customers
250,000 Community
$200 Million Rev
31 Offices
18 Countries
Raised $151 Million
(or more)
Cash-Flow Neutral
2005 2013 2015 2016 2017 2018
Timeline Slide

47. 47
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48. 48
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49. 49
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competency alt
Growth
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Equal
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Cultural
acceptance
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Cost high
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50. 50
RPA
champion
Question and
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guidelines
Reply ResourceRedesign Redesign alt Refresh Refresh 2 Remove Remove alt RPA business
analyst
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developer
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infrastructure
engineer
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support
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architect
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integration
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team collab
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sponsor
Thank youSolutions altSpeed Stopwatch Stories Success Technology
alt 2
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alt 3
Support Survey Tap Touch Target TechnologyTechnology
alt
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Time saver User User-OTM Validate Vendor Welcome Zoom inVisibility offVisibility onWarning Web
expert
Web
scraping
Web
testing
Zoom outVector
anchor
Money
square
Location
pin
Lock Lock
open
Macro
recording
Managing Mobile
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circle
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call
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concept 2
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sector
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Icons (Mono) 2
50

51. 51
Robot AttendedRobot Unattended
Orchestrator Studio
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Enterprise-
competency alt
Growth
Certification
Course
foundation 2
Equal
Health
Chart
Course
foundation 3
Error
Heart
Clock
Course-
orchestrator
Exception
handling
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app
CRM
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Hourglass
Code
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program
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data entry
AI
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audit
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First-mention
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Label alt
Cost low
Editor
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partner-up alt
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2
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Decrease
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Advanced
OCR
Institutionalize
Cloud
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Cultural
acceptance
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51

52. 52
RPA
champion
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Reply ResourceRedesign Redesign alt Refresh Refresh 2 Remove Remove alt RPA business
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infrastructure
engineer
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support
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architect
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integration
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integration alt
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team collab
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sponsor
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alt 2
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alt 3
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alt
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Time saver User User-OTM Validate Vendor Welcome Zoom inVisibility offVisibility onWarning Web
expert
Web
scraping
Web
testing
Zoom outVector
anchor
Money
square
Location
pin
Lock Lock
open
Macro
recording
Managing Mobile
device
Money Managing alt Map Map alt Map alt 2 Media Mismatch Money
circle
Manage
documents
QAMonitoringMonitoring altNote OCR Phone
call
Proof of
concept
Proof of
concept 2
Pivot Plugin Process
identification
Process
identification 2
Project Promoter
bullhorn
Public
sector
OCR alt
Icons (White) 2
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