Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'flags' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1539, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 131, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1419, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 976, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 794, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1570, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 835, in finalize
self._build_writer(self.schema)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 861, in _build_writer
self.pa_writer = pq.ParquetWriter(
~~~~~~~~~~~~~~~~^
self.stream,
^^^^^^^^^^^^
...<3 lines>...
**get_parquet_column_options(self._features, self._schema),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pyarrow/parquet/core.py", line 1115, in __init__
self.writer = _parquet.ParquetWriter(
~~~~~~~~~~~~~~~~~~~~~~^
sink, schema,
^^^^^^^^^^^^^
...<20 lines>...
max_rows_per_page=max_rows_per_page,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
**options)
^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2430, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'flags' with no child field to Parquet. Consider adding a dummy child field.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1401, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1579, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
json dict | __key__ string | __url__ string |
|---|---|---|
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
0,
381.7067307692308
],
[
8.486562942008486,
384.2715700141443
],
[
7.496463932107496,
406.76096181046677
],
[
0,... | MIST/test/gt/Bengali__31052023_L_GH010002_image_000001 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
702.7710843373493,
281.12449799196787
],
[
574.6586345381526,
267.8714859437751
],
[
587.9116465863453,
239.7590361445783
],
... | MIST/test/gt/Bengali__31052023_L_GH010002_image_000035 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
371.79577464788724,
80.98591549295774
],
[
369.60960960960966,
38.73873873873874
],
[
428.4593023255814,
94.47674418604652
],
... | MIST/test/gt/Bengali__31052023_L_GH010002_image_000068 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
5.564516129032332,
830.6451612903226
],
[
40.24193548387104,
827.4193548387096
],
[
38.62903225806459,
861.2903225806451
],
... | MIST/test/gt/Bengali__31052023_L_GH010002_image_000084 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
1.2244897959183672,
833.4693877551019
],
[
51.0204081632653,
835.9183673469387
],
[
52.24489795918367,
853.8775510204081
],
... | MIST/test/gt/Bengali__31052023_L_GH010002_image_000123 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
1166.3084112149534,
223.3644859813084
],
[
1171.4485981308412,
121.9626168224299
],
[
1435.9345794392525,
126.63551401869158
],
... | MIST/test/gt/Bengali__31052023_L_GH010002_image_000143 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
455.2631578947368,
200.52631578947367
],
[
456.0897435897436,
164.67948717948718
],
[
522.1052631578947,
162.6315789473684
],
... | MIST/test/gt/Bengali__31052023_L_GH010002_image_000159 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
0,
444.8275862068965
],
[
45.83690987124471,
443.77682403433477
],
[
42.75862068965526,
477.8325123152709
],
[
0,... | MIST/test/gt/Bengali__31052023_L_GH010002_image_000185 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
3.2926829268293236,
124.7560975609756
],
[
63.04878048780492,
95.48780487804879
],
[
154.51219512195127,
128.41463414634148
],
... | MIST/test/gt/Bengali__31052023_L_GH010002_image_000186 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
{
"version": "5.6.0a0",
"flags": {},
"shapes": [
{
"points": [
[
867.3684210526316,
611.0526315789473
],
[
881.0526315789473,
581.3157894736842
],
[
955.2631578947368,
584.2105263157895
],
... | MIST/test/gt/Bengali__31052023_L_GH042821_image_000062 | hf://datasets/ProtonPrat/G-Mineru-testsets@15e84c48273bba64b8f6e86a62f2c4d517fa2b2e/MIST/MIST_test.tar |
End of preview.
G-Mineru scene-text test sets: UberText and MIST
This repository holds the exact test-set copies that the G-Mineru benchmark (E046) uses.
Each test set is one uncompressed tar file. Paths in the tar start at the Datasets/ root.
| File | Benchmark name | Images | GT files | Size |
|---|---|---|---|---|
UberText/UberText_test_1Kx1K.tar |
ubertext_1k |
10,157 | 10,157 | 2.5GB |
MIST/MIST_test.tar |
mist_test |
1,985 | 1,985 | 5.6GB |
ubertext_1k is the full UberText test split at 1Kx1K resolution, not a 1,000-image sample.
Each folder also has SOURCE.json (where the data came from), filelist.txt (tar members),
and the root has SHA256SUMS for the tar files.
Extract
huggingface-cli download ProtonPrat/G-Mineru-testsets --repo-type dataset --local-dir testsets
cd testsets && sha256sum -c SHA256SUMS
mkdir -p Datasets
tar -C Datasets -xf UberText/UberText_test_1Kx1K.tar # -> Datasets/UberText/test/1Kx1K/
tar -C Datasets -xf MIST/MIST_test.tar # -> Datasets/MIST/test/{images,gt}/
UberText
- Source: Uber-Text,
https://s3-us-west-2.amazonaws.com/uber-common-public/ubertext/Uber-Text.zip(ETag301b299696c03e24106d0be1052736c0-6301, checked). Onlytest/1Kx1Kis here. - GT format:
truth_img_N.txtforimg_N.jpg. One line for each text instance: polygon x y pairs (closed), a tab, the text, a tab, the label (for exampleStreetNumber,None). 33,942 instances; 6,746 of them are multi-word phrases, which our word-level scorer treats as "do not care". - Licence: CC BY-SA 4.0 (Uber). This copy is under the same licence. The authors blurred faces and licence plates as best effort.
@inproceedings{UberText,
author = {Ying Zhang and Lionel Gueguen and Ilya Zharkov and Peter Zhang and Keith Seifert and Ben Kadlec},
title = {Uber-Text: A Large-Scale Dataset for Optical Character Recognition from Street-Level Imagery},
booktitle = {SUNw: Scene Understanding Workshop - CVPR 2017},
year = {2017}
}
MIST
- Source:
https://huggingface.co/datasets/Saumya-Mundra/MIST-Dataset, revision873f04eedf0b8c11199ded41edc0b298c7391927. Only thetestsplit (images/,gt/) is here. - GT format: one LabelMe-style JSON for each image.
shapes[].pointsis the polygon,careis 0 for "do not care" regions,scriptis the script name (or null). There are no transcriptions: MIST is a detection-only benchmark here. - Licence: the source card says "other". It says: "The repository is released for research use subject to the terms and permissions specified by the MIST authors and the original data sources. Please check with the authors before commercial redistribution or redistribution of raw imagery." Use this copy for research only, and follow the terms of the source repository. If you are a MIST author and want this copy removed, open a discussion on this repository.
@article{mist2025,
title = {MIST: Multilingual Incidental Dataset for Scene Text Detection},
author = {Mundra, Saumya and Mondal, Ajoy and Jawahar, C.V.},
journal = {WACV},
year = {2026}
}
- Downloads last month
- 63