MonkeyOCR v1.5: Making Complex PDFs Parseable — AI Innovations and Insights 90

If you’ve ever worked with real scanned documents or PDFs, you’ve likely run into this mess: a table nested inside another table, split awkwardly across pages, with images or formulas jammed between rows. You run it through OCR, and suddenly cells are missing, the reading order is scrambled, or content is misclassified entirely. This isn’t just a bug. It’s a structural headache.

This post might offer a useful perspective.

Why Document Parsing Still Trips Up, Even in 2025

Here’s why parsing real documents is still harder than it looks:

• Complex Tables Are a Pain Point: Nested tables, merged or split cells, cross-page layouts, and embedded content like images or formulas all introduce brittleness into the parsing process. Any one of these can throw off structural recognition or cause misalignment between content and layout.

• Traditional Pipelines Are Fragile: Legacy OCR pipelines usually split the job into several stages (layout detection, formula detection, text recognition, table parsing, and reading order prediction), each handled independently. The problem? Errors in one stage often bleed into the next. If a bounding box is even slightly off in the layout stage, good luck getting the reading order or table structure right downstream.

• End-to-End Models Are Computationally Heavy: One might hope end-to-end large models could solve everything in a single pass. But high-res document images translate into tens of thousands of visual tokens, and self-attention layers scale quadratically. This eats up compute fast. Even the best foundation models struggle to balance speed and fidelity when faced with dense documents.