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Datasets:
comradev7
/
Scenic-Font-Analysis

Tasks:
Image Classification
Languages:
Hindi
Japanese
Korean
Tags:
fonts
typography
scene-text
Synthetic
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
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:    CastError
Message:      Couldn't cast
schema_version: string
track: string
created_utc: string
image_count: int64
columns: list<item: string>
  child 0, item: string
style_unified_counts: struct<regular: int64, bold: int64>
  child 0, regular: int64
  child 1, bold: int64
split_counts: struct<train: int64, val: int64, test: int64>
  child 0, train: int64
  child 1, val: int64
  child 2, test: int64
language_counts: struct<japanese: int64, hindi: int64, korean: int64>
  child 0, japanese: int64
  child 1, hindi: int64
  child 2, korean: int64
ground_truth_sha256: string
image_layout: string
images_archive_sha256: string
images_archive_members: int64
notes: list<item: string>
  child 0, item: string
about: string
label_spaces: struct<style_unified: list<item: string>, split: list<item: string>>
  child 0, style_unified: list<item: string>
      child 0, item: string
  child 1, split: list<item: string>
      child 0, item: string
layout: struct<track_dir_pattern: string, required_files: list<item: string>, optional_files: list<item: str (... 43 chars omitted)
  child 0, track_dir_pattern: string
  child 1, required_files: list<item: string>
      child 0, item: string
  child 2, optional_files: list<item: string>
      child 0, item: string
  child 3, image_location: string
  child 4, note: string
repo_type: string
unique_key: list<item: string>
  child 0, item: string
ground_truth.csv: struct<columns: list<item: struct<name: string, type: string, required: bool, description: string, a (... 29 chars omitted)
  child 0, columns: list<item: struct<name: string, type: string, required: bool, description: string, allowed: list<ite (... 12 chars omitted)
      child 0, item: struct<name: string, type: string, required: bool, description: string, allowed: list<item: string>>
          child 0, name: string
          child 1, type: string
          child 2, required: bool
          child 3, description: string
          child 4, allowed: list<item: string>
              child 0, item: string
to
{'schema_version': Value('string'), 'repo_type': Value('string'), 'about': Value('string'), 'layout': {'track_dir_pattern': Value('string'), 'required_files': List(Value('string')), 'optional_files': List(Value('string')), 'image_location': Value('string'), 'note': Value('string')}, 'ground_truth.csv': {'columns': List({'name': Value('string'), 'type': Value('string'), 'required': Value('bool'), 'description': Value('string'), 'allowed': List(Value('string'))})}, 'label_spaces': {'style_unified': List(Value('string')), 'split': List(Value('string'))}, 'unique_key': List(Value('string')), 'notes': List(Value('string'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              schema_version: string
              track: string
              created_utc: string
              image_count: int64
              columns: list<item: string>
                child 0, item: string
              style_unified_counts: struct<regular: int64, bold: int64>
                child 0, regular: int64
                child 1, bold: int64
              split_counts: struct<train: int64, val: int64, test: int64>
                child 0, train: int64
                child 1, val: int64
                child 2, test: int64
              language_counts: struct<japanese: int64, hindi: int64, korean: int64>
                child 0, japanese: int64
                child 1, hindi: int64
                child 2, korean: int64
              ground_truth_sha256: string
              image_layout: string
              images_archive_sha256: string
              images_archive_members: int64
              notes: list<item: string>
                child 0, item: string
              about: string
              label_spaces: struct<style_unified: list<item: string>, split: list<item: string>>
                child 0, style_unified: list<item: string>
                    child 0, item: string
                child 1, split: list<item: string>
                    child 0, item: string
              layout: struct<track_dir_pattern: string, required_files: list<item: string>, optional_files: list<item: str (... 43 chars omitted)
                child 0, track_dir_pattern: string
                child 1, required_files: list<item: string>
                    child 0, item: string
                child 2, optional_files: list<item: string>
                    child 0, item: string
                child 3, image_location: string
                child 4, note: string
              repo_type: string
              unique_key: list<item: string>
                child 0, item: string
              ground_truth.csv: struct<columns: list<item: struct<name: string, type: string, required: bool, description: string, a (... 29 chars omitted)
                child 0, columns: list<item: struct<name: string, type: string, required: bool, description: string, allowed: list<ite (... 12 chars omitted)
                    child 0, item: struct<name: string, type: string, required: bool, description: string, allowed: list<item: string>>
                        child 0, name: string
                        child 1, type: string
                        child 2, required: bool
                        child 3, description: string
                        child 4, allowed: list<item: string>
                            child 0, item: string
              to
              {'schema_version': Value('string'), 'repo_type': Value('string'), 'about': Value('string'), 'layout': {'track_dir_pattern': Value('string'), 'required_files': List(Value('string')), 'optional_files': List(Value('string')), 'image_location': Value('string'), 'note': Value('string')}, 'ground_truth.csv': {'columns': List({'name': Value('string'), 'type': Value('string'), 'required': Value('bool'), 'description': Value('string'), 'allowed': List(Value('string'))})}, 'label_spaces': {'style_unified': List(Value('string')), 'split': List(Value('string'))}, 'unique_key': List(Value('string')), 'notes': List(Value('string'))}
              because column names don't match

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Scenic Font Analysis — multi-track dataset

Shared dataset repository for the Samsung PRISM Scenic Font Analysis project. Each contributor publishes their own track so the data can be merged into a single training set without schema drift.

Repository layout 

README.md
schema.json
track_a/
  ground_truth.csv      # required
  manifest.json         # recommended (counts + checksums)
  images.tar.gz         # required; members are named image_filename at the archive root
track_b/                # another contributor, identical structure
track_c/                # another contributor, identical structure
tools/                  # helper scripts (also in the project repo)

Why a tar and not a folder? Hugging Face rejects any repository directory with more than 10,000 files. Tracks with thousands of images therefore ship a single images.tar.gz; tools/pull_all_tracks.py extracts it back into images/ automatically (only tracks with <= 10,000 images may use a loose images/ folder).

The contract (see schema.json)

Every track_*/ground_truth.csv must have exactly these columns:

column notes
image_filename basename of the file in images/
language e.g. hindi / japanese / korean
font_family
font_style raw source label (kept for provenance)
style_unified one of regular, bold, italic, bolditalic
split one of train, val, test
is_clean boolean
text label text

style_unified is the single unified taxonomy. Track A's 9 raw weights are collapsed by taxonomy.unify_style(): thin/extralight/light/regular/medium -> regular, semibold/bold/extrabold/black -> bold, and italic/oblique labels become italic / bolditalic.

Add your own track

  1. Install + log in:
    pip install -U "huggingface_hub[cli]"
hf auth login
  2. Ask Vignesh to add you: repo -> Settings -> Collaborators (repo is private).
  3. Put your labels in a CSV and stage a validated track folder:
    python tools/export_track.py --track b --images <your_images_dir> \
    --ground-truth <your_labels.csv> --out staging
    Writes staging/track_b/{ground_truth.csv,manifest.json,images/} and enforces the schema above.
  4. Upload (resumable, safe to re-run):
    python tools/push_repo.py --repo comradev7/Scenic-Font-Analysis --folder staging

Consume / merge all tracks 

python tools/pull_all_tracks.py --repo comradev7/Scenic-Font-Analysis --out data
python tools/merge_tracks.py --root data --out data/merged/ground_truth.csv

merge_tracks.py validates each track, normalizes labels, adds a track column and writes one merged CSV (with an image_path column relative to the dataset root) ready for training.
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Total file size:
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