Enhanced Document Understanding: Boosting Efficiency with AI-Powered Automation
Doc Understanding is an intelligent system designed to enhance efficiency by accelerating the document scanning and analysis process. Using reliable AI technologies, it improves the effectiveness of processing paperwork by automating the analysis of increasing volumes of documentation. This automation helps reduce costs, increase margins, and enable our customers to provide better services.
Scenario
Imagine if software robots could understand documents, extracting, interpreting, and processing data from PDFs, images, handwriting, and scans. Doc Understanding is an automatic document recognition system that enhances paperwork processing effectiveness by automating documentation analysis with AI technologies. Our solution uses modules and microservices to streamline the entire paperwork processing procedure through four key modules:
- Downloader: Downloads documents from company systems via REST API and stores the data in the Doc Understanding system.
- Worker: Processes files and related documents by communicating with MongoDB, S3 Storage, and Google Cloud Platform AI services, highlighting the required entities.
- Presentation: Allows operators (validators) to supervise the documents processed by the Doc Understanding system and enables quick display and analysis of validated information.
- Uploader: Uploads the documents validated by Doc Understanding via REST API.
How it Works
A user initiates the document validation process by uploading documents to the system. Based on business-defined conditions, the documents are imported from Doc Understanding to MongoDB via a REST API exposed by the originating system and stored on Minio. Doc Understanding optimizes and verifies that the documents haven’t been processed before proceeding with the analysis. Each document is sent to Google to apply powerful optical character recognition (OCR) and various AI techniques to extract useful information, which is then stored in MongoDB. The system submits the document for review and approval, fueling the system’s automatic learning. The document is then filed as processed or incomplete (with reasons), and the outcome is sent back to the original system.
For instance, an insurance company needed an intelligent system to automatically recognize documents included in the healthcare expense reimbursement process and their content, thus enabling liquidators to work more efficiently. Doc Understanding was implemented to perform semi-automatic clearance of health insurance claims, speeding up the claim process. The main documents and information were highlighted and made easily accessible, enhancing the procedure through AI.
