High-Fidelity Document OCR and Layout Reconstruction
Pain Points (Public)
Organizations frequently accumulate legacy paper scans and flattened multi-page PDFs containing mixed elements like nested tables, header-footer bands, and flowable body copy. Standard optical character recognition tools routinely break complex column alignments and merge disparate table cells into unformatted text blobs, requiring labor-intensive manual reformatting and verification in Microsoft Word and Excel.
Suggested Approach (Public)
Implement a layout-aware OCR and document parsing pipeline that segments multi-modal document regions, uses specialized table-extraction models to detect row-column coordinates, and programmatically reconstructs clean, editable styles in .docx and .xlsx formats with preserved typography and cell structures.
Metrics (Public)
Statistics window：Monthly 2026-09-27 – 2026-10-26
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Public Demand Evidence · 2 task(s)
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