Other sources provide images, PDF files, and other documents. The app can also return the results to the front-end browser. When the extraction quality meets requirements, the data enters Azure Cosmos DB for downstream application consumption. If the quality is below a specified threshold, the app flags the data for manual verification. The App Service back-end application uses the confidence values to check the extraction quality. Form Recognizer also assigns confidence values to the extracted data.
The response from Form Recognizer contains raw OCR data and structured extractions. The back-end application posts a request to a Form Recognizer REST API endpoint that uses one of these models: This load balancer runs with Azure Web Application Firewall, which helps to protect the application from common attacks and vulnerabilities. The solution routes the documents to that application through Azure Application Gateway. Azure App Service hosts a back-end application.
The documents contain images or are in PDF format. Data ingestion and extractionĭocuments are ingested through a browser at the front end of a web application. The following sections describe the various stages of the data extraction process. Architectureĭownload a Visio file of this architecture. Natural language processing (NLP) models and custom models enrich the data. The solution uses Azure Form Recognizer for the structured extraction of data.
This article outlines a scalable and secure solution for building an automated document processing pipeline.
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