Measuring temporal gains in assisted document transcription

Transcribing ancient manuscripts is a very time consuming task mainly done by specialists like historians and paleographers. In order to minimize time spent on transcribing, computing tools can be used to assist researchers in this process, but, to our knowledge, there are no studies that evaluate and quantify precisely their actual benefits in terms of human time spent on the transcription. This paper presents a study on quantifying the temporal efficiency of different transcription and segmentation workflows. The main contribution of our work is the extension of a popular existing open-source text transcription web platform: eScriptorium with tools and methods to measure time spent on the transcription task. We explore and compare the efficiency of three workflows: fully manual, using a default annotation model, and using a finetuned model for both ancient manuscript segmentation and transcription. In our experiments, we aim to observe and compare the temporal gains achieved by each workflow, highlighting the trade-offs between manual and automated processing of transcriptions. The paper describes the design of the tracing layer, presents the methodology and the results. This work is conducted as part of the ChEDiL1 French ANR project.