Spatial Reader for Academic Papers & Articles
Read research papers 3x faster.
Scrolling back and forth to find Figure 3 and its references can be annoying. Transform flat dense PDFs into a spatial multi-panel workspace where images, tables, and references stay coupled directly alongside the text.
Auto-Coupled Figures & Tables
IDE Multi-Panel Workspace
100% Private In-Browser
Drop your research paper PDF here or click to browse
Instantly decouples figures, tables, and sections into an interactive spatial workspace.
PDF format only
Max file size: 25MB
Recently Read Papers
0 papers
Local reading history saved on your browser (sorted by most recent access).
Loading recent paper history...
Designed for reading research papers
Eliminate reading friction with coupled visual figures, spatial layout docking, and zero unnecessary bloat.
Figure & Reference Coupling
Figures, charts, and tables open right alongside the text paragraph referencing them. Zero back-and-forth scrolling.
Multi-Window IDE Layout
Powered by Dockview. Decouple dense papers into resizable, side-by-side modular panels for text, figures, and notes.
Deep Layout Decomposition
High-speed layout parsing decomposes complex two-column academic PDFs into clean structured blocks.
Local History & Hash Verification
Track recently read papers via SHA-256 fingerprinting saved locally in browser storage with full privacy.
How SpatialPDF Works
Transform static, flat documents into an interactive multi-window workspace in three simple steps.
01
Upload & Fingerprint
Drag and drop any PDF. We verify SHA-256 hashes locally to bypass redundant server parsing and protect privacy.
02
Spatial AI Layout Parsing
High-speed AI models decompose dense multi-column PDFs into individual text sections, figure crops, and math blocks.
03
Multi-Panel Workspace
View figures side-by-side with referenced text in dockable, resizable IDE windows without lost context.
Got Questions?
Frequently Asked Questions
Everything you need to know about SpatialPDF, security, and layout parsing.
Our backend uses deep AI computer vision models to segment two-column academic papers and complex PDFs into discrete structural blocks (paragraphs, figure images, figure captions, tables, and equations). These blocks are parsed into structured JSON and sent to your browser.