The webinar on Unlocking the Power of Document Extraction with AI discussed how AI improves
document processing, reducing costs by up to 80% and achieving 95-99% accuracy. AI enables
faster handling of data-heavy documents across industries like finance and healthcare. Tools like
AWS Textract, Azure Document Intelligence, and Google Document AI were reviewed, with advice
on tool selection based on needs. A demo highlighted AI’s impact on automation, minimizing
manual effort and enhancing data accuracy.
Session overview:
- 40-minute interactive workshop, LIVE & open to all.
- Speakers: Vijay Roy, CEO
Amit Mahto, Software Engineer.
Key Highlights-
Introduction to Document Extraction:
- Overview of document extraction and the need for advancements beyond traditional OCR.
- Discussion on why document extraction is becoming critical across industries, especially for
processing high volumes of structured and unstructured data like insurance claims, KYC forms,
medical invoices, etc.
Challenges with Traditional Methods:
- Manual document processing is time-consuming, prone to human error, and costly—averaging
$20-$25 per document. - Industries often face inefficiencies due to unstructured data formats and manual extraction.
Benefits of AI-Powered Document Extraction:
- Cost savings: Reducing document processing costs by up to 80% (from $25 to $5 per
document). - Increased accuracy: AI-based extraction offers 95-99% accuracy, minimizing errors and
enhancing processing speed. - Efficiency: With AI, documents can be processed in minutes instead of days or weeks, leading to
better decision-making and customer experiences.
Industry Applications:
- Industries like finance, healthcare, insurance, and real estate benefit significantly through cost
reduction, faster decision-making, and improved accuracy. - Case example: A claims processing firm saved 30% in document handling costs, amounting to
$3 million in annual savings.
OCR Tools and Technologies:
- Overview of leading OCR tools
- AWS Textract: Known for 97% accuracy and integration within the AWS ecosystem.
- Azure Document Intelligence: Supports custom models with high language flexibility and
integration within Azure. - Google Document AI: Offers customizable models and supports a wide range of languages.
- Open-source (Tesseract): Suitable for on-premises solutions but with lower accuracy and
limited to image formats.
Choosing the Right Tool:
- Selection criteria: Accuracy, speed of implementation, language support, integration needs, and
data sensitivity. - For rapid deployment with high accuracy, cloud solutions like AWS, Azure, or Google are ideal,
while open-source tools are recommended for on-premises needs and sensitive data handling.
Demo of an AI-Powered Document Processing Framework: - Live demo showcasing an agentic framework using AWS Textract to process various document
types (e.g., bank statements, tenant information, mortgage data). - Demonstrated how AI-driven automation reduces human effort, ensuring accurate data
extraction and generating monthly reports with minimal manual intervention.
Q&A and Insights:
- Addressed questions on integration options, scaling document processing frameworks, and
how AI-powered extraction can support compliance and audit readiness across industries.
