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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    FileNotFoundError
Message:      https://huggingface.co/datasets/katanaml/cord/resolve/main/dataset.zip/CORD/train
Traceback:    Traceback (most recent call last):
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 411, in _info
                  await _file_info(
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 827, in _file_info
                  r.raise_for_status()
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1005, in raise_for_status
                  raise ClientResponseError(
              aiohttp.client_exceptions.ClientResponseError: 404, message='Not Found', url=URL('https://huggingface.co/datasets/katanaml/cord/resolve/main/dataset.zip/CORD/train')
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 485, in compute_first_rows_response
                  rows = get_rows(
                File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 120, in decorator
                  return func(*args, **kwargs)
                File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 176, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 917, in __iter__
                  for key, example in ex_iterable:
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 113, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/katanaml--cord/fa0960248a7d19cf19675785d5d3dd9eab83b4aea9274b97943d534be56d8a91/cord.py", line 119, in _generate_examples
                  for file in sorted(os.listdir(ann_dir)):
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 70, in wrapper
                  return function(*args, use_auth_token=use_auth_token, **kwargs)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 533, in xlistdir
                  fs, *_ = fsspec.get_fs_token_paths(path, storage_options=storage_options)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/core.py", line 586, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 252, in filesystem
                  return cls(**storage_options)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 76, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 53, in __init__
                  self.fo = fo.__enter__()  # the whole instance is a context
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/core.py", line 102, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1135, in open
                  f = self._open(
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 350, in _open
                  size = size or self.info(path, **kwargs)["size"]
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 114, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 99, in sync
                  raise return_result
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 54, in _runner
                  result[0] = await coro
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 424, in _info
                  raise FileNotFoundError(url) from exc
              FileNotFoundError: https://huggingface.co/datasets/katanaml/cord/resolve/main/dataset.zip/CORD/train

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CORD: A Consolidated Receipt Dataset for Post-OCR Parsing

CORD dataset is cloned from clovaai GitHub repo

  • Box coordinates are normalized against image width/height
  • Labels with very few occurrences are replaced with O:
replacing_labels = ['menu.etc', 'menu.itemsubtotal', 
                    'menu.sub_etc', 'menu.sub_unitprice', 
                    'menu.vatyn', 'void_menu.nm', 
                    'void_menu.price', 'sub_total.othersvc_price']

Check for more info Sparrow

Citation

CORD: A Consolidated Receipt Dataset for Post-OCR Parsing

@article{park2019cord,
  title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
  author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
  booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
  year={2019}
}

Post-OCR parsing: building simple and robust parser via BIO tagging

@article{hwang2019post,
  title={Post-OCR parsing: building simple and robust parser via BIO tagging},
  author={Hwang, Wonseok and Kim, Seonghyeon and Yim, Jinyeong and Seo, Minjoon and Park, Seunghyun and Park, Sungrae and Lee, Junyeop and Lee, Bado and Lee, Hwalsuk}
  booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
  year={2019}
}
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