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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:      [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'

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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 development
  • test/ — 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 language
  • Pairings = 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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