Machines Reading Maps

"This is unreasonably cool, and so visually pleasing, it nearly tickles. It also feels like I’m looking at one of those moments where everything changes."

Machines Reading Maps Summit April 20-21, 2023

Invite-only Stakeholder Introduction to MRM
Public Introduction to MRM
Invite-only Community-building discussion
MRM/Recogito Workshop

How does MRM work?

Image Cropping

Text Spotting

Using TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild.
TESTR is particularly effective when dealing with curved text-boxes where special cares are needed for the adaptation of the traditional bounding-box representations.

Different from OCR, which is adept at extracting separate words, where here we are interested in FULL LOCATION PHRASES

Human Annotations

SynMap+ Training Data

Use a generative adversarial style transfer network (CycleGAN) to convert an OSM image to the historical style,
Associate the font, style and placement strategy according to the underlying geographical features
Use "rule-based" labeling from the QGIS PAL API to place the text labels on the synthetic map background,
designed an approach to automatically generate the polygon, centerline and local height annotation for the text labels.

SynthText Training Data

https://www.robots.ox.ac.uk/~vgg/data/scenetext/

PatchTextSpotter & PatchtoMapMerging

Coordinate Converter

PostOCR & Entity Linker

https://searchworks.stanford.edu/view/vn901vj0926

Next Steps: Build Software

"This past summer, a CESTA intern georeferenced 100+ [19th century] South African maps (at farm level). It would be great to harvest the place names and make them digitally searchable. Is that something we could discuss please?"

-Grant Parker, Classics Dept.