Datasets:

biglam
/

early_printed_books_font_detection

Tasks:

Image Classification

Sub-tasks: multi-label-image-classification

Size Categories: 10K<n<100K

Annotations Creators: expert-generated

License: cc-by-nc-sa-4.0

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The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    FileNotFoundError
Message:      https://zenodo.org/api/files/6a702b1c-7856-4a3c-9f48-895f8aa42b7e/fontgroupsdataset-a.zip
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 417, in _info
                  await _file_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 837, in _file_info
                  r.raise_for_status()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1005, in raise_for_status
                  raise ClientResponseError(
              aiohttp.client_exceptions.ClientResponseError: 429, message='Too Many Requests', url=URL('https://zenodo.org/api/files/6a702b1c-7856-4a3c-9f48-895f8aa42b7e/fontgroupsdataset-a.zip')
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 327, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 271, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 307, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 937, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.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/biglam--early_printed_books_font_detection/9235bfffae98d5e462f35ca37e6f94e79e1ff1d624dbcf4cad71718b669c6abf/early_printed_books_font_detection.py", line 119, in _generate_examples
                  for file in Path(directory).rglob("*"):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 667, in glob
                  fs, *_ = fsspec.get_fs_token_paths(xjoin(posix_path, pattern), storage_options=storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 586, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 253, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 76, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 58, in __init__
                  self.fo = fo.__enter__()  # the whole instance is a context
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 102, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1151, in open
                  f = self._open(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 356, in _open
                  size = size or self.info(path, **kwargs)["size"]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 115, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 100, in sync
                  raise return_result
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 55, in _runner
                  result[0] = await coro
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 430, in _info
                  raise FileNotFoundError(url) from exc
              FileNotFoundError: https://zenodo.org/api/files/6a702b1c-7856-4a3c-9f48-895f8aa42b7e/fontgroupsdataset-a.zip

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Dataset Card for Early Printed Books Font Detection Dataset

Dataset Summary

This dataset is composed of photos of various resolution of 35'623 pages of printed books dating from the 15th to the 18th century. Each page has been attributed by experts from one to five labels corresponding to the font groups used in the text, with two extra-classes for non-textual content and fonts not present in the following list: Antiqua, Bastaπrda, Fraktur, Gotico Antiqua, Greek, Hebrew, Italic, Rotunda, Schwabacher, and Textura.

[More Information Needed]

Supported Tasks and Leaderboards

The primary use case for this datasets is

multi-label-image-classification: This dataset can be used to train a model for multi label image classification where each image can have one, or more labels.
image-classification: This dataset could also be adapted to only predict a single label for each image

Languages

The dataset includes books from a range of libraries (see below for further details). The paper doesn't provide a detailed overview of language breakdown. However, the books are from the 15th-18th century and appear to be dominated by European languages from that time period. The dataset also includes Hebrew.

[More Information Needed]

Dataset Structure

This dataset has a single configuration.

Data Instances

An example instance from this dataset:

{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3072x3840 at 0x7F6AC192D850>,
 'labels': [5]}

Data Fields

This dataset contains two fields:

image: the image of the book page
labels: one or more labels for the font used in the book page depicted in the image

Data Splits

The dataset is broken into a train and test split with the following breakdown of number of examples:

train: 24,866
test: 10,757

Dataset Creation

Curation Rationale

The dataset was created to help train and evaluate automatic methods for font detection. The paper describing the paper also states that:

data was cherry-picked, thus it is not statistically representative of what can be found in libraries. For example, as we had a small amount of Textura at the start, we specifically looked for more pages containing this font group, so we can expect that less than 3.6 % of randomly selected pages from libraries would contain Textura.

Source Data

Initial Data Collection and Normalization

The images in this dataset are from books held by the British Library (London), Bayerische Staatsbibliothek München, Staatsbibliothek zu Berlin, Universitätsbibliothek Erlangen, Universitätsbibliothek Heidelberg, Staats- und Universitäatsbibliothek Göttingen, Stadt- und Universitätsbibliothek Köln, Württembergische Landesbibliothek Stuttgart and Herzog August Bibliothek Wolfenbüttel.

[More Information Needed]