The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
image_size: list<item: int64>
child 0, item: int64
language: string
num_units: int64
units: list<item: struct<unit_type: string, role: string, polygon: list<item: list<item: double>>, language (... 270 chars omitted)
child 0, item: struct<unit_type: string, role: string, polygon: list<item: list<item: double>>, language: string, f (... 258 chars omitted)
child 0, unit_type: string
child 1, role: string
child 2, polygon: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 3, language: string
child 4, font: string
child 5, font_size: int64
child 6, curvature: double
child 7, bubble_shape: string
child 8, fill_style: string
child 9, has_tail: bool
child 10, words: list<item: struct<text: string, polygon: list<item: list<item: double>>, color_rgba: list<item: int6 (... 22 chars omitted)
child 0, item: struct<text: string, polygon: list<item: list<item: double>>, color_rgba: list<item: int64>, languag (... 10 chars omitted)
child 0, text: string
child 1, polygon: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 2, color_rgba: list<item: int64>
child 0, item: int64
child 3, language: string
child 11, panel_index: int64
composition_mode: string
num_panels: int64
gutter_px: int64
border_px: int64
panel_rects: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
source_task_id: int64
seed: int64
image_file: string
to
{'image_size': List(Value('int64')), 'language': Value('string'), 'num_units': Value('int64'), 'units': List({'unit_type': Value('string'), 'role': Value('string'), 'polygon': List(List(Value('float64'))), 'language': Value('string'), 'font': Value('string'), 'font_size': Value('int64'), 'curvature': Value('float64'), 'bubble_shape': Value('string'), 'fill_style': Value('string'), 'has_tail': Value('bool'), 'words': List({'text': Value('string'), 'polygon': List(List(Value('float64'))), 'color_rgba': List(Value('int64')), 'language': Value('string')})}), 'composition_mode': Value('string'), 'source_task_id': Value('int64'), 'seed': Value('int64'), 'image_file': 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 478, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
image_size: list<item: int64>
child 0, item: int64
language: string
num_units: int64
units: list<item: struct<unit_type: string, role: string, polygon: list<item: list<item: double>>, language (... 270 chars omitted)
child 0, item: struct<unit_type: string, role: string, polygon: list<item: list<item: double>>, language: string, f (... 258 chars omitted)
child 0, unit_type: string
child 1, role: string
child 2, polygon: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 3, language: string
child 4, font: string
child 5, font_size: int64
child 6, curvature: double
child 7, bubble_shape: string
child 8, fill_style: string
child 9, has_tail: bool
child 10, words: list<item: struct<text: string, polygon: list<item: list<item: double>>, color_rgba: list<item: int6 (... 22 chars omitted)
child 0, item: struct<text: string, polygon: list<item: list<item: double>>, color_rgba: list<item: int64>, languag (... 10 chars omitted)
child 0, text: string
child 1, polygon: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 2, color_rgba: list<item: int64>
child 0, item: int64
child 3, language: string
child 11, panel_index: int64
composition_mode: string
num_panels: int64
gutter_px: int64
border_px: int64
panel_rects: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
source_task_id: int64
seed: int64
image_file: string
to
{'image_size': List(Value('int64')), 'language': Value('string'), 'num_units': Value('int64'), 'units': List({'unit_type': Value('string'), 'role': Value('string'), 'polygon': List(List(Value('float64'))), 'language': Value('string'), 'font': Value('string'), 'font_size': Value('int64'), 'curvature': Value('float64'), 'bubble_shape': Value('string'), 'fill_style': Value('string'), 'has_tail': Value('bool'), 'words': List({'text': Value('string'), 'polygon': List(List(Value('float64'))), 'color_rgba': List(Value('int64')), 'language': Value('string')})}), 'composition_mode': Value('string'), 'source_task_id': Value('int64'), 'seed': Value('int64'), 'image_file': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
"--- license: cc-by-4.0 task_categories:
object-detection
image-to-text language:
ja
en
ko tags:
manga
manhwa
synthetic
ocr
multilingual pretty_name: Mangium size_categories:
10K<n<100K
I am Indomie, an independent developer who is just starting out in this field. Driven by my passion for manga and my desire to build tools around it, I hit a major roadblock: the severe shortage of free, open-source datasets available for training models—especially datasets specifically tailored for manga itself. Because of this, I decided to build and generate this dataset myself to bridge the gap and help out others who are interested in this space.
Note: Since this is my very first project of this kind, the data might not be perfect or entirely error-free. It remains a work in progress, and I will be continuously improving future versions based on your feedback and usage.
-About Me & Project Context I am Indomie, an independent developer who is just starting out in this field. Driven by my passion for manga and my desire to build tools around it, I hit a major roadblock: the severe shortage of free, open-source datasets available for training models—especially datasets that support Asian languages with complex, vertical text directions. Because of this, I decided to build and generate this dataset myself to bridge the gap and help out others who are interested in this space.
Note: Since this is my very first project of this kind, the data might not be perfect or entirely error-free. It remains a work in progress, and I will be continuously improving future versions based on your feedback and usage.
-Language DistributionI balanced the languages in this dataset to cover different reading systems (both vertical and horizontal alignment) according to the following breakdown:LanguageDataset ProportionSupported Reading DirectionJapanese (JA)40%Vertical (majority) / HorizontalKorean (KO)30%Horizontal / VerticalEnglish (EN)30%Horizontal
- Generated Graphic Components (Dataset Features) I programmatically overlaid custom text and dialogue elements onto reference images. These components include:
Text Balloons: Multiple shapes including elliptical bubbles (standard dialogue), cloud-like shapes (internal thoughts), jagged/spiky bubbles (screaming/loud dialogue), and rectangular narrative boxes.
Visual Sound Effects (SFX): Free-form, angled, and curved texts that mimic expressive, hand-drawn sound effects in the background.
- Dataset Output Layout The outputs are structured in a standardized directory format, making it easy to plug them directly into your training pipelines:
images/: Contains the final composited images in .jpg format.
annotations/: Individual .json files corresponding to each image name, containing:
High-precision polygon coordinates (Polygons) defined at both the unit/paragraph level and individual word level.
The actual text content (Transcription) mapped to each polygon.
Structural classification labels (e.g., dialogue bubble, sound effect, narrative box).
manifest.jsonl: A consolidated manifest file that maps each image path to its annotations on a single line, allowing for rapid batch loading during training.
- Target Applications You can use my dataset to train and evaluate models on several key tasks:
Improving Text Detection: Training models to locate text lines that overlap with background art or are written in highly stylized, angled, or non-standard fonts.
Developing Manga OCR: Training classifiers to recognize multilingual characters oriented in both vertical and horizontal directions within the same page.
Layout Analysis & Segmentation: Helping models distinguish between regular dialogue bubbles, narrative boxes, and background sound effects to automate cleaning or translation pipelines.
- License I have released this dataset under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means you are completely free to use, modify, and build upon this data for academic or commercial purposes, as long as you give appropriate credit to me.
- Downloads last month
- 43