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.
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In Calamari short notation:conv=40:3\(\,\times \,\)3, pool=2\(\,\times \,\)2, conv=60:3\(\,\times \,\)3, pool=2\(\,\times \,\)2, lstm=200, dropout=0.5.
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In Calamari short notation:conv=40:3\(\,\times \,\)3, pool=2\(\,\times \,\)2, conv=60:3\(\,\times \,\)3, pool=2\(\,\times \,\)2, conv=120:3\(\,\times \,\)3, pool=2\(\,\times \,\)2,lstm=200, lstm=200, lstm=200, dropout=0.5.
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Acknowledgement
The authors would like to thank our student research assistants Lisa Gugel, Kiara Hart, Ursula Heß, Annika Müller, and Anne Schmid for their extensive segmentation and transcription work as well as Maximilian Nöth and Maximilian Wehner for supporting the data preparation.
This work was partially funded by the German Research Foundation (DFG) under project no. 460665940.
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Reul, C., Tomasek, S., Langhanki, F., Springmann, U. (2022). Open Source Handwritten Text Recognition on Medieval Manuscripts Using Mixed Models and Document-Specific Finetuning. In: Uchida, S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science, vol 13237. Springer, Cham. https://doi.org/10.1007/978-3-031-06555-2_28
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