Why Traditional OCR Fails on Complex Medical Tables (And How Dual-AI Fixes It)

The Structural Limitations of Legacy OCR

Old OCR (Optical Character Recognition) tools look at pixels and attempt to draw rigid grids. However, modern medical tables often use whitespace, indents, and implied visual hierarchies instead of actual drawn lines to separate columns. This causes OCR to fail catastrophically.

Why the Grid Breaks

If a clinical trial table has a merged cell that spans three columns (e.g., "Adverse Events - Grade 3"), legacy OCR sees a gap and breaks the column alignment for all subsequent rows. This is why you end up with data scrambled across random Excel cells.

The Vision-Language Model (VLM) Revolution

TargetMesh entirely abandons rigid OCR in favor of advanced Vision-Language Models. Our AI actually reads the report visually, understanding the semantic relationship between elements.

• Semantic Understanding: The AI understands that a "10mg" dosage belongs to "Aspirin," even if the spacing between the words is irregular.
• Handling Invisible Borders: It reconstructs the intended logical table even when visual borders are missing.
• Resolving Multi-Line Rows: When a description spills over to a second line, TargetMesh keeps it unified in a single Excel cell, preventing row misalignment.

Stop fixing broken tables manually. Let AI do the heavy lifting of spatial intelligence.