The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type string to null
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2315, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2071, in cast_array_to_feature
_c(children[name] if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2155, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Multilingual OCR Annotation Dataset
Publisher: DataCluster Labs
1. Overview
The Multilingual OCR Annotation Dataset is a collection of annotated text data designed to support research and development in Optical Character Recognition (OCR), multilingual text recognition, and document intelligence.
The dataset covers multiple languages and writing systems, including Hebrew, Thai, Japanese, and Arabic, providing diverse linguistic representations for exploring OCR capabilities across different scripts.
It aims to support the development and evaluation of AI systems capable of detecting, recognizing, and extracting text from multilingual visual content.
2. Dataset Details
| Attribute | Description |
|---|---|
| Dataset Name | Multilingual OCR Annotation Dataset |
| Publisher | DataCluster Labs |
| Languages | Hebrew, Thai, Japanese, Arabic |
| Data Category | OCR and Text Annotation |
| Primary Domain | Computer Vision and Document AI |
| Intended Use | AI research, model training and evaluation |
| Release Type | Dataset release |
3. Language Coverage
The dataset includes annotations across the following languages:
| Language | Script | Language Code |
|---|---|---|
| Hebrew | Hebrew | he |
| Thai | Thai | th |
| Japanese | Japanese | ja |
| Arabic | Arabic | ar |
These languages introduce different text-processing challenges, including right-to-left writing, complex character structures, script-specific segmentation, and varying word-boundary conventions.
4. Annotation and Data Characteristics
The dataset focuses on annotated textual information for OCR and computer vision applications.
Depending on the annotation format, the data may include:
- Text Transcription: Recognized textual content.
- Text Localization: Position of text within an image or document.
- Bounding Boxes or Polygons: Geometric representation of text regions.
- Language Identification: Association of samples with their respective languages.
- Structured Annotations: Machine-readable representations of OCR labels.
The exact annotation fields and structures depend on the individual dataset release.
5. Potential Applications
The dataset can support research and experimentation in:
- Multilingual OCR: Recognizing text across different languages and scripts.
- Text Detection: Identifying and locating text regions in images.
- Text Recognition: Converting image-based text into machine-readable content.
- Document Intelligence: Extracting and understanding information from documents.
- Computer Vision: Developing models for visual text understanding.
- Multilingual AI: Improving language-aware visual processing systems.
- OCR Benchmarking: Comparing OCR model performance across different scripts.
6. Research Significance
Multilingual OCR presents challenges because writing systems differ in character structure, directionality, spacing, typography, and segmentation requirements.
For example:
- Arabic and Hebrew use right-to-left writing systems.
- Thai features complex character combinations and spacing conventions.
- Japanese combines multiple writing systems, including Hiragana, Katakana, and Kanji.
A diverse annotated dataset can help researchers investigate these challenges and develop more robust OCR systems across different languages.
7. Intended Audience
This dataset is intended for:
- AI and machine learning researchers
- OCR model developers
- Computer vision engineers
- Document AI researchers
- Multilingual NLP practitioners
- Data scientists and annotation teams
- Academic institutions and research organizations
8. Custom OCR Annotation Services
Alongside dataset development, DataCluster Labs provides custom OCR annotation and dataset creation services for organizations, researchers, and AI teams.
Our services include:
- Multilingual OCR annotation
- Text detection and transcription
- Bounding box and polygon annotation
- Language-specific text annotation
- Custom OCR dataset creation
- Dataset structuring and quality checks
We can work with your existing image and document collections or create custom OCR datasets based on your project requirements.
9. License and Usage
Please refer to the license specified for this dataset release before downloading, modifying, redistributing, or using the data for commercial purposes.
The applicable license and usage permissions should be confirmed for each release.
10. Citation
Please use the following citation when referencing this dataset:
DataCluster Labs. Multilingual OCR Annotation Dataset.
For academic publications and technical reports, include the Hugging Face repository URL and the date of access.
11. Publisher and Contact
DataCluster Labs
Website: https://datacluster.ai/
Email: sales@datacluster.ai/
For dataset access, custom OCR annotation requirements, research collaborations, and dataset development enquiries, please contact our team.
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