Comments (3)
Thanks! The signature of wf.list_steps() changed, so, yes, you should do print(wf.list_steps()).
Please note that the workflow is about preprocessing the vudnc data, this has nothing to do with the icdar 2017 shared task. Also, I do not recommend using the vudnc data, because it is very noisy. But if you do want to preprocess it anyway, you should do
cwltool ochre/cwl/vudnc-preprocess-pack.cwl --archive path/to/vudnc/archive
from ochre.
Thanks! The signature of wf.list_steps() changed, so, yes, you should do print(wf.list_steps()).
Please note that the workflow is about preprocessing the vudnc data, this has nothing to do with the icdar 2017 shared task. Also, I do not recommend using the vudnc data, because it is very noisy. But if you do want to preprocess it anyway, you should do
cwltool ochre/cwl/vudnc-preprocess-pack.cwl --archive path/to/vudnc/archive
You are correct. I meant that I was not able to run vudnc-preprocess-pack.cwl.
For good results in english, do you recommend using the english monograph partition of ICDAR? I trained with both monograph and the periodical partitions in separated but the validation accuracy and loss were not good (and also the tests I made).
I would like to help with some additional documentation to improve reproducibility, but I need a roadmap of how to get significant results (mainly for english documents).
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Unfortunately, ochre is not (yet) fit for training good ocr post-correction models. I plan to work on it in the future, but only as a hobby project. So no promises there!
Generally speaking, the OCR post-correction datasets are small. That's why I'm making a list of them, so they can be used for generalization. I don't think that training on the English monograph data will give you a model that will work on other data, because OCR errors tend to depend on time period, font, the ocr software that was used, etc.
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Related Issues (17)
- Make separate commands for the diffrent neural network architectures
- Error during preprocessing HOT 2
- About OCR_aligned and Lost or missing text HOT 3
- Permanent failure with VU recepie HOT 2
- Additional OCR Post correction datasets HOT 2
- Pretrained models HOT 1
- /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
- 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
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