Scalable and Cost-effective Serverless Architecture for Information Extraction Workflows

Abstract

Information extraction from an image or scanned document is a complex and challenging process since it involves recognizing various visual structures such as tables, boxes, logos, text, charts, etc. Hence, the content extraction applications contain a pipeline of multiple computer vision algorithms, APIs, and models. Deploying such applications for document processing requires a resilient system to deliver high performance. Such applications can be deployed on cloud to leverage the flexible infrastructure and multiple supporting services available there. In this paper, we discuss a scalable and high performance architecture using a serverless platform for deploying information extraction workflows consisting of multiple APIs and computer vision models. Our experiments show that the use of a serverless platform results in a scalable, cost-effective, and low latency deployment of such workflows. Moreover, we discuss the performance and cost trade-offs while choosing cloud services and their configuration. We also show that the use of workload characterization-based performance and cost models to find the optimal serverless instance configuration results in a significant deployment cost reduction.