# TrOCR Kurrent-Model 19th century Handwritten Text Recognition model for 19th century German.
Part of the developments at the Digital Humanities@University of Bern. Developed by Jonas Widmer and Tobias Hodel in conjunction with researchers and institutions mentioned below.
Base model: microsoft/trocr-base-handwritten
Train Lines: 292'997
Eval Lines: 7'513
Test Lines: 15'817
Epochs: 19.66 / 20
Eval CER: 0.02827
Test CER: 0.02655
Finetuned on Kurrent-dataset, containing:
- Material from the State Archives of Zurich ("Regierungsratsprotokolle"), provided by the State Archives of Zurich
- Lecture notes of Humboldt Lectures, provided by the Berlin-Brandenburgian Academy of Sciences
- Diary of Eugen Huber, provided by the University of Zurich
- Handwriting and Copies by and of Gottfried Semper (provided by the respective research project at ETH Zürich and USI Mendrisio)
- Konzilsprotokolle, University of Greifswald (19th century)
- as well as many other smaller collections/examples
The model has not been extensively tested. Potential biases are still to be identified.
Input: one line, not a page
This is a VisionEncoderDecoderModel for line images: it reads one cropped
text line per call, which is also what the CER above was measured on. A whole
page is not an input it has — pass one and it returns a fragment of it.
Segment the page first (kraken, eScriptorium, Transkribus, or any line
segmenter) and pass the crops one at a time. In the DH Bern serving stack the
model is registered level: line and the gateway segments with kraken before
calling it; the reasoning and the measurements behind that rule are in
thodel/serving-atr-inference#165.