Cost-effective End-to-end Information Extraction for Semi-structured Document Images
This work addresses cost and complexity issues for organizations deploying large-scale document processing systems, though it appears incremental as it builds on existing sequence generation approaches.
The authors tackled the high development and maintenance costs of multi-module information extraction systems for semi-structured document images by transitioning to an end-to-end model, achieving competent performance in real-world production.
A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost. One can instead consider an end-to-end model that directly maps the input to the target output and simplify the entire process. However, such generation approach is known to lead to unstable performance if not designed carefully. Here we present our recent effort on transitioning from our existing pipeline-based IE system to an end-to-end system focusing on practical challenges that are associated with replacing and deploying the system in real, large-scale production. By carefully formulating document IE as a sequence generation task, we show that a single end-to-end IE system can be built and still achieve competent performance.