Human-Machine Collaborative Transcription

How is this task different than other Zooniverse transcription projects?
This project uses the Zooniverse Transcription Task, but with a twist: the 'first pass' annotations + transcriptions have been generated by a machine learning algorithm.

Why am I being asked to do this?
There are two reasons we need your help! The first is to help us determine the accuracy of the line detection and transcription models we are using. The second is to give us feedback on this style of 'correct-a-machine' workflow for text transcription projects.

Why don't you just run a regular transcription project and have humans provide the transcriptions?
Our aim is to increase the quality of automated line detection, so volunteers can focus on transcribing, rather than drawing lines. Our previous research into transcription methods increased the quality of results by reducing the need for line aggregation, and we know from the Talk boards that people much prefer transcribing to annotating! This work will help us get a better sense of where to focus our efforts as we think about the text transcription tools we provide in the Project Builder.

Will correct-a-machine projects reduce the need for volunteer assistance entirely?
Absolutely not! As you will likely be able to tell from participating in this project, machine annotation and transcription alone has a long way to go. Our main goal is to identify where machine learning can help alleviate some of the effort, particularly in more complex task types, so that volunteers can focus on more interesting efforts!

How will you use the results?
These images have already been transcribed as part of another project, but we still need this data to carry out our research. We will compare the transcription data from this workflow to gold-standard transcription data in order to measure whether this approach impacts the quality of transcription data produced through this method.