Document extraction to accelerate application processing
This case study details the development of a document extraction prototype to streamline benefits application processing through automated data capture and classification.
The case study outlines a research-driven effort to reduce the burden of manual document processing in public benefits programs.
The team built a prototype using OCR and NLP to extract and classify data from user-submitted documents—like W-2s or DD214s—even from low-quality images. Over multiple testing rounds, the system achieved high accuracy and processing speed improvements, setting the stage for future integration with agency workflows. The study emphasizes the value of modular, government-ready tools that address persistent administrative challenges while improving service delivery for applicants and staff alike.
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