piwik-script

Intern
Zentrum für Philologie und Digitalität "Kallimachos"

Paper für DAS2022 akzeptiert

09.03.2022

Unser Paper "Open Source Handwritten Text Recognition on Medieval Manuscripts using Mixed Models and Document-Specific Finetuning" wurde als Vortrag für die DAS2022 akzeptiert. Die DAS2022 Konferenz der IAPR (International Association for Pattern Recognition) findet dieses Jahr im Hybridformat in La Rochelle in Frankreich statt. Das vorläufige Paper ist hier abrufbar.

In Kooperation mit Kollegen des Lehrstuhls für deutsche Philologie, Ältere Abteilung und dem CIS München wurden Modelle für die automatische Texterkennung auf mittelalterlichen Handschriften erstellt und ausführlich evaluiert. Mehr Informationen zum Trainingskorpus und Modelltraining finden sich hier.

Abstract:
"This paper deals with the task of practical and open source Handwritten Text Recognition (HTR) on German medieval manuscripts.
We report on our efforts to construct mixed recognition models which can be applied out-of-the-box without any further document-specific training but also serve as a starting point for finetuning by training a new model on a few pages of transcribed text (ground truth). To train the mixed models we collected a corpus of 35 manuscripts and ca. 12.5k text lines for two widely used handwriting styles, Gothic and Bastarda cursives. Evaluating the mixed models out-of-the-box on four unseen manuscripts resulted in an average Character Error Rate (CER) of 6.22%. After training on 2, 4 and eventually 32 pages the CER dropped to 3.27%, 2.58%, and 1.65%, respectively.
While the in-domain recognition and training of models (Bastarda model to Bastarda material, Gothic to Gothic) unsurprisingly yielded the best results, finetuning out-of-domain models to unseen scripts was still shown to be superior to training from scratch.
Our new mixed models have been made openly available to the community."