Capture and Convert Structured Data from Scanned Case Documents to Support Advanced Analysis and Trend Forecasting
Description
The Targeting & Special Projects Unit (DOIT) has identified a critical need to capture, convert, and process both structured and unstructured text from scanned case documents to support advanced analysis and trend forecasting. This initiative will leverage an optical character recognition (OCR) solution capable of extracting both typed and handwritten content from diverse sources, including medical notes, invoices, and statements. Captured text will be transformed into a standardized, machine-readable format (e.g., CSV) and integrated into a relational database. From there, advanced analytical techniques will be applied to reveal hidden structures, patterns, and relationships within the data. By unlocking this information, we aim to enhance our ability to anticipate trends, strengthen investigative strategies, and move toward a more predictive, data-driven approach
Detailed example
The anticipated output for this AI use case will include text for documentation and CSV for excel spreadsheet analytical work.
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Save valuable investigative time that can be used to focus on the results of the analysis. Conducting more comprehensive analysis on the data will improve trend forecasting, strengthen investigative strategies, and support a more predictive, data-driven approach.
Controls / human review
ATO: Not reported; PIA: Not published