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Book Annotation Classification Engine

A UCLA Collections Lab/BuildUCLA project: computer vision experiments to identify and classify annotations in digitized printed books in IIIF-hosted collections.

Status: Experimenting with different neural network architectures and evaluating their performances on a dataset of over 3000 images.

Want to help us build our training data? Tag and classify annotation here:


Project team

  • Dawn Childress
  • Pete Broadwell
  • Andrew Wallace
  • Johnny Ho
  • Emily Chen
  • Jonathan Quach
  • Morgan Madjukie
  • Rahul Malavalli

More Info on Some Files in this Path


Intentionally not track pycache .pyc and .ipynb_checkpoints since they do not provide convenient information about changes that may have occured to the code.

Standard file to be included to treat folders as packages (list of modules) so as to allow modularization

Binarizes an image or a folder of images (NOTE: must provide an output path if binarizing an entire folder)


$ python img.png #one image $ python --out /Users/John/Desktop/outputFolder /Users/John/Desktop/inputFolder

Randomly generates "number" square subsamples of side length "px" from file(s) "files" where "number" "px" and "files" are from user input.