Pretrained models for the ANN-based post-correction component.
All models use cor-asv-ann-train --width 512 --depth 2 and were initialised with weights from a language model via --init-model, then pretrained on 200k lines of clean text (input=output) from DTA, and then retrained via --load-model on GT4HistOCR and OCR-D GT, processed by various OCR models (input=OCR with confidence, output=GT). The latter is allowed to change all weights (not just fine-tuning) and does not reset the encoder layer weights (--reset-encoder).
- dta19.Fraktur4: on 19th century Fraktur texts (GT4HistOCR/corpus/dta19) for the Tesseract 4 model
script/Fraktur - pre19.Fraktur4: on 15-18th century blackletter texts (GT4HistOCR others, OCR-D) for the Tesseract 4 model
script/Fraktur - pre19.deu-frak3: on 15-18th century blackletter texts (GT4HistOCR others, OCR-D) for the Tesseract 3 model
deu-frak - pre19.Latin4: on 15-18th century blackletter texts (GT4HistOCR others, OCR-D) for the Tesseract 4 model
script/Latin - pre19.deu4: on 15-18th century blackletter texts (GT4HistOCR others, OCR-D) for the Tesseract 4 model
deu - pre19.incunabula: on 15-18th century blackletter texts (GT4HistOCR others, OCR-D) for the Ocropus 1 model
incunabula.pyrnnincluded in GT4HistOCR - pre19.latinhist: on 15-18th century blackletter texts (GT4HistOCR others, OCR-D) for the Ocropus 1 model
latinhist.pyrnnincluded in GT4HistOCR
- gt4histocr.s-ſ: on GT4HistOCR ground truth degraded by replacing
ſintosin the input (encouraging the network to learn its reconstruction)
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