Bounding Box Bonanza

We (the Zooniverse team!) are learning how to be most efficient in our transcription projects. To do this we are training a machine learning model to recognize where text is placed on an image. To train the model, we need volunteer annotations that can help show where there is text on an image. Specifically, this work is to gather data that will allow us to improve textual line prediction accuracy on Handwritten Text Recognition (HTR) projects, with the eventual goal of reducing the need for a line-drawing step altogether, while still allowing researchers and volunteers to benefit from the clarity and resulting data provided by a line-by-line classification structure.

This project has been used to support the following efforts:

• 2025: "Optimizing the Human-Machine System for Citizen Science" - created with support from a 2023 Collaborative Research: Human-Centered Computing award from the National Science Foundation. The University of Minnesota and Adler Planetarium Zooniverse teams are conducting research and experiments with the aim of removing classification inefficiencies that are currently a bottleneck for Handwritten Text Recognition (HTR) efforts.