Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '/src/services/worker/2566/png/en.png'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2567, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2103, in __iter__
batch = formatter.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2254, in decode_batch
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1508, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 175, in decode_example
image = PIL.Image.open(path)
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3431, in open
fp = builtins.open(filename, "rb")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: '/src/services/worker/2566/png/en.png'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for HuggingFaceFW/Multilingual-Image-Translation-Groups
Dataset Summary
The IMG-MT dataset is a multilingual dataset of images containing text, where visually identical images differ only in the language of the embedded text. The texts appearing at the same positions across images are translations of each other.
The dataset was created to support the evaluation of systems that perform text detection, translation and rendering in images, such as pipelines for image-based machine translation or OCR-based translation systems.
All images originate from Wikimedia Commons and are grouped into language groups consisting of visually identical images that differ only in the language of the text.
The dataset contains 26 languages and supports evaluation of translation between multiple language pairs.
Dataset Details
Dataset Description
The dataset consists of pairs of images containing translated text. Each pair contains:
- a source image with text in one language
- a target image with the translated text in another language
Bounding boxes for text regions are provided, along with the corresponding source and translated text strings.
Dataset characteristics
- Images contain text embedded in graphics (e.g., diagrams, maps, educational illustrations).
- Images in a group are visually identical except for the language of the text.
- Text blocks are aligned across images using bounding boxes.
Dataset Sources
- Source of images: Images were collected from Wikimedia Commons
Uses
The dataset is suitable for:
- Evaluation of image translation pipelines
- Evaluation of OCR + machine translation systems
- Benchmarking text detection in images
- Testing text rendering after translation
- Multilingual visual-language research
Dataset Structure
Dataset Organization
The dataset is divided into two parts:
dev/— development set used for tuning and developmenttest/— test set used for final evaluation
Each of these sets contains several numbered folders ({languageGroupNumber}/), each representing a language group.
These folders store images (in png/ folder) and reference JSON files ({sourceLangCode}-{targetLangCode}.json).
| Folder/File | Description |
|---|---|
dev/ |
Development dataset |
test/ |
Test dataset |
{languageGroupNumber}/ |
Group of images that differ only by language |
png/ |
Images in different languages |
{sourceLangCode}-{targetLangCode}.json |
Reference translation data |
Data Format
Reference translation data are stored in JSON format with the following structure:
{
"source_language": "language code",
"source_PNG": {
"size": {
"width": px,
"height": px
},
"path_to_image": "path",
"wikimedia_url": "url"
},
"text_bounding_box": [
{
"x": float,
"y": float,
"w": float,
"h": float
}
],
"texts": ["string"],
"target_language": "language code",
"translated_texts": ["string"],
"target_PNG": {
"size": {
"width": px,
"height": px
},
"path_to_image": "path",
"wikimedia_url": "url"
}
}
| Field | Description |
|---|---|
source_language |
Language code of the source text |
source_PNG |
Information about the source image |
text_bounding_box |
Bounding boxes of text regions |
texts |
Source texts extracted from the image |
target_language |
Language code of the translated text |
translated_texts |
Translated texts corresponding to the source |
target_PNG |
Information about the translated image |
The lists: text_bounding_box, texts, translated_texts are aligned — items at the same index correspond to the same text region.
Dataset Statistics
The dataset contains 26 languages and multiple language groups of varying sizes.
Example language statistics:
| Language | Code | Groups | Pairings |
|---|---|---|---|
| English | en | 435 | 1530 |
| French | fr | 204 | 796 |
| German | de | 160 | 668 |
| Italian | it | 120 | 604 |
| Hungarian | hu | 79 | 368 |
| Polish | pl | 67 | 398 |
| Russian | ru | 67 | 296 |
| Spanish | es | 62 | 266 |
| Czech | cs | 59 | 314 |
| Turkish | tr | 50 | 260 |
| Swedish | sv | 49 | 284 |
| Dutch | nl | 37 | 200 |
| Finnish | fi | 31 | 184 |
| Norwegian | no | 28 | 146 |
| Romanian | ro | 28 | 146 |
| Latin | la | 9 | 70 |
Groups= number of image groups containing the given languagePairings= number of language pairs in the dataset where the given language is either the source or target language
Smaller language groups also include languages such as: Kannada, Indonesian, Chinese, Korean, Georgian, Japanese, Vietnamese, Albanian, Belarusian and Estonian.
Language groups range in size from 2 to 12 images.
Dataset Creation
Source Data
Images were collected from Wikimedia Commons.
Data Collection and Processing
The collection process involved:
Searching for images that exist in multiple languages
Selecting images that are visually identical except for the text language
Grouping images into language groups
Extracting text regions using bounding boxes
Recording corresponding source and translated texts
Each language group contains images representing the same graphic with text translated into different languages.
Bias, Risks and Limitations
Several limitations should be considered:
Language distribution is imbalanced, with English and major European languages being overrepresented.
Some languages appear only in a small number of examples.
The dataset mainly contains diagram-like images rather than natural scene text.
The dataset size is relatively small, which limits its use for large-scale training.
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