dh-unibe/trocr-essoins-middle-latin

<h1>Textrecognition Model for Essoins (England) in Latin</h1>

Part of the developments within the Flow-Project. Developed by Jonas Widmer, Christopher Kuhlmann, and Melvin Wilde.

Base model: magistermilitum/tridis_HTR v1

Train Lines: 15356

Eval Lines: 394

Test Lines: 2288

Epochs: 14.1667 / 20

Eval CER: 0.0544

Test CER: 0.0622

<h2>Testresults with CERberus</h2>

CERberus on GitHub with explanations CERberus gets 2288 lines of the test set and evaluates them.

The handwritten texts in Latin (with some Middle-English and Anglo-Norman wording) that were used for training are from the 13th and 14th centuries. They come from England and were written in 'Court Hand', also known as 'Anglicana'. They come from the 'Court of Common Pleas', the second highest court of the time, and deal primarily with civil disputes, such as inheritances or dowries, and from the Justices, which also dealt with civil pleas, but covered crown pleas as well.

The model has not been extensively tested.

Errors often occur in the Punctuation, which itself has an error rate of 44.44% which mostly consits of missed ‧ dots.

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.