AI-based Document Digitization Solution Using Azure Cognitive Services: 4 weeks PoC

The manual extraction of data from documents is a time-consuming process and prone to errors. Many industries, such as healthcare, insurance, and finance, face challenges in handling a large volume of unstructured data in paper-based formats. Inefficient data extraction leads to delayed decision-making and loss of productivity.

Affine’s AI-based Document Digitization Solution addresses the challenges of inconsistent report designs and enables efficient data extraction from digitized documents. The solution is designed to extract structured information from standard as well as custom document templates. The extracted data can be stored in Azure Cosmos DB for further analysis purposes.

The proposed solution utilizes various Azure services, including Azure Form Recognizer, Azure Blob, Azure Cosmos DB, etc. The Azure Form Recognizer service automatically identifies and extracts relevant information from structured documents such as invoices, receipts, and forms. The extracted data is then stored in Azure Cosmos DB, a globally distributed multi-model database service, for further analysis.

This Proof-of-Concept implementation can be expanded to your production environment.

Benefits:

1. Automated Process: The solution automates the document digitization process, reducing the need for error-prone manual data extraction processes.
2. Increased Productivity: Digitized documents are 100% text searchable, making it easier to find and retrieve the data, thereby increasing productivity.
3. Scalable: The solution is scalable and can accommodate various document template designs, making it easier to digitize documents across different departments and organizations.

Agenda:

• Business & Data Understanding: Understand the report layout and expected output format for storing data.
• Setup & Configuration: Set up and configure the Azure environment, including Blob Storage and Azure Form Recognizer.
• AI Model: Label documents, consume Form Recognition service, validate the model using holdout data, and make improvements.
• AE Layer: Create storage layer with schema design, develop pipelines to ingest model output into storage layer, deploy a model using Azure Form Recognizer's built-in deployment, test and validate.

Why Affine

• Enabling business-focused data science, AI, and BI development with deep domain expertise.
• Affine believes in faster design and deployment through key differentiators- Experimentation Focus and Speed to Value.