Comments (3)
The task ochre performs is a supervised machine learning task. So, without gold standard, you can't create aligned data or train a (supervised) model.
from ochre.
Sorry,maybe I expressed not clearly.
I mean after supervised training(for training data,we must have gold standard),how can I use this trained ochre model for actual OCR post-correction tasks? Because for actual tasks,we usually don't have gold standard and desire to get corrected text which similiar to the gold standard. On this occasion,how can I get OCR_aligned from the raw OCR_input of the actual tasks?
Thanks!
from ochre.
The README specifies how to use a trained model to do post correction: https://github.com/KBNLresearch/ochre#ocr-post-correction
If you want to calculate performance for this text, you'd still need to have ground truth/gold standard.
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Related Issues (17)
- Make separate commands for the diffrent neural network architectures
- Update workflows for extracting datasets to accept zipfile as input
- Working without aligned file HOT 2
- Using ochre to evaluate synthetic ocr post processing dataset generation HOT 3
- Error in align_output_to_input HOT 3
- All chars assumption HOT 2
- Unsatisfying results HOT 2
- Where to start? HOT 1
- Error during preprocessing HOT 2
- Permanent failure with VU recepie HOT 2
- Additional OCR Post correction datasets HOT 2
- Pretrained models HOT 1
- print error - ICDAR2017_shared_task_workflows.ipynb HOT 3
- /usr/bin/python: dateutil 2.5.0 is the minimum required version HOT 1
- Issues in testing HOT 3
- Is test and training data format different. HOT 3
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