legacy-document-evidence-intake

Document normalization for legacy system modernization converts varied formats like Excel, Word, Visio, and PDFs into structured outputs such as Markdown, CSV, and PNG. It uses OCR and structural extraction to ensure extracted fragments are traceable and machine-consumable, facilitating structured modernization knowledge generation.

Yes, you can extract text and coordinates from scanned PDFs and Visio diagrams. The process uses OCR engines and PDF renderers to perform structural extraction, assigning evidence coordinates to every fragment to ensure traceability within the machine-consumable evidence package.

Yes, you need LibreOffice, an OCR engine, and a PDF renderer installed to perform document normalization. These dependencies are required for file conversion, rendering PDFs, and extracting text from images and legacy documents during the evidence coordination process.

The best way to automate legacy enterprise document conversion into Markdown and CSV is by running an automated intake process. This normalizes diverse file types, performs structural extraction, and outputs structured manifests with quality gates and evidence coordinates for downstream analysis.

Yes, document normalization works with image screenshots and PowerPoint files. It converts these formats into normalized outputs like PDF, PNG, and SVG, using OCR to extract text and assigning evidence coordinates to maintain traceability within the evidence package.

Legacy document extraction requires quality gates and evidence coordination to ensure accuracy and traceability. By assigning evidence coordinates to every extracted fragment, the process guarantees that normalized outputs are reliable, machine-consumable, and suitable for structured modernization analysis.