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MOCR: From Text-Only OCR to Parse-Anything Intelligence
If you work with documents long enough, you start noticing how often the most important information isn’t in the paragraphs. It hides in charts, tables, diagrams, and all the parts traditional OCR still treats like decorative noise.
Why Traditional OCR Is No Longer Enough
Traditional OCR and many document parsing pipelines are still largely text-centric: they recover text well, but usually do not structurally parse information-dense graphics.
When confronted with charts, diagrams, icons, or UI components, they usually treat these elements as mere image fragments and store them as pixel blocks. As a result, a significant portion of structural and semantic information within documents is lost.
Figure 1 captures the main shift: traditional OCR keeps graphics as raster regions, while Multimodal OCR represents text and eligible visual symbols in a unified serialized format, using plain text, table markup, LaTeX, or SVG depending on the element type.
MOCR: Not Just Reading Text, but Reconstructing Graphics
MOCR, short for Multimodal OCR, aims to parse anything found in documents.
- For textual regions, it outputs plain text, table markup, or…
