Many businesses today should understand the importance of using AI to improve the business processes and delight customers. We will show how you can use AI & ML technologies to get insights from processed data

1. Using AI to Solve Business Challenges
Marvin Heng
@hmheng
www.techconnect.io

2. Azure AI

3. Azure AI
AI Apps & Agent Knowledge Mining Machine Learning
Azure Search
Form Recognizer

4. Form Recognizer

5. Form Recognizer
• Applies advanced machine learning to accurately extract text, key/value pairs, and
tables from documents.
• Tailors its understanding to your documents, both on-premises and in the cloud.
Easily extract text and structure, with simple REST API.
• Capable to run on the edge devices with container services. (link)

6. Form Recognizer
• Mixed Mode Support
(Printed Text + Handwritten Text)
• Pre-built Receipts
• Off the shelf model
• Available as a managed service
• Empowered by Azure OCR service

7. Lots of various types of forms

8. Why do we need Form Recognizer?
Time consuming manual
entry
Not efficient Long business process

9. Challenges of Data Extraction from Forms
Various Types of Forms Document Quality –
Scanned, Fax, Photo
Different Types of Text
Forms – Printed, hand-
written signatures, tables,
check marks.

10. Form Recognizer Service
Data extraction in any business process that intakes forms and outputs structured daa
INPUT: Forms
{
"status": "Succeeded",
"recognitionResults": [
{
"page": 1,
“clockwiseOrientation": 355.77,
"width": 3024,
"height": 4032,
"unit": "pixel",
…
…
"understandingResults": [
{
"pages": [ 1 ],
"fields": {
"Subtotal": {
"valueType": "numberValue",
"value": 1098.99,
"text": "1098.99",
"elements": [ { "$ref":
"#/recognitionResults/0/lines/15/words/1" } ]
},
}
OUTPUT: Structured Data
Key Pair Values and Tables

11. Form Recognizer Comprised of
Pre-Built
(Pre-trained model)
Tailored to your forms
(Custom Training)

12. Form Recognizer Solution
Train
Analyze
Form Recognizer
Model
Data Extraction
{
"status": "Succeeded",
"recognitionResults": [
{
"page": 1,
“clockwiseOrientation": 355.77,
"width": 3024,
"height": 4032,
"unit": "pixel",
…
…
"understandingResults": [
{
"pages": [ 1 ],
"fields": {
"Subtotal": {
"valueType": "numberValue",
"value": 1098.99,
"text": "1098.99",
"elements": [ { "$ref":
"#/recognitionResults/0/lines/15/words/1" } ]
},
}
Extracted Key Pair
Value

13. Form Recognizer Service

14. Demo – Custom Analyze
Form Recognizer with custom training dataset

15. Receipt Solution
• Merchant Name
• Merchant Address
• Merchant Phone Number
• Transaction Date
• Transaction Time
• Subtotal
• Tax
• Total

16. Demo – Receipts
Receipt Automation with Form Recognizer + SignalR + Angular

17. Example of Use Case – Intelligent Expense Report

18. Benefits
• Easily Extract Data with no manual labeling required
• Only a handful of example documents needed to train a model (5 forms)
• Tailored to your forms
• Pre-Built Receipts data extraction
• Enterprise-grade security and privacy
• Available as a managed service or cognitive services container

19. Useful links
• https://github.com/hmheng/AILabs
• https://medium.com/@hmheng
• https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer/overview
• https://github.com/buildaidemos

20. Q&A
Marvin Heng
@hmheng
www.techconnect.io