- 2023.10.29: Our new paper for joint text spotting and layout analysis is accepted to WACV 2024!
- 2023.08.23: Oral presentation at ICDAR 2023 for our competition.
- 2023.05.17: Competition report available here.
- 2023.04.14: We have released the results (click here) of ICDAR 2023 Competition on Hierarchical TextDetection and Recognition. Congratulations and thanks for all the efforts!
- 2022.12.12: We will be hosting ICDAR 2023 Competition on Hierarchical TextDetection and Recognition with HierText! The competition will be held on the Robust Reading Comprehension website, including two tasks, (1) Hierarchical Text Detection and (2) Word-Level End-to-End Text Detection and Recognition. See the website for more info.
- 2022.08.17: The evaluation server is launched on Robust Reading Comprehension. Now you can submit the result of your method on this website.
- 2022.05.11: The Out-of-Vocabulary competition is launched as part of the Text-in-Everything workshop at ECCV 2022. HierText is incorporated to construct the benchmark dataset.
- 2022.06.02: Code and weights for the unified detector model are released in TensorFlow Model Garden.
- 2022.03.03: Paper accepted to CVPR 2022.
HierText is the first dataset featuring hierarchical annotations of text in natural scenes and documents. The dataset contains 11639 images selected from the Open Images dataset, providing high quality word (~1.2M), line, and paragraph level annotations. Text lines are defined as connected sequences of words that are aligned in spatial proximity and are logically connected. Text lines that belong to the same semantic topic and are geometrically coherent form paragraphs. Images in HierText are rich in text, with average of more than 100 words per image.
We hope this dataset can help researchers developing more robust OCR models and enables research into unified OCR and layout analysis. Check out our paper for more details.
In the paper, we also propose a novel method called Unified Detector, that unifies text detection and layout analysis. The code and pretrained checkpoint is now available at this repository
First clone the project:
git clone https://github.com/google-research-datasets/hiertext.git
(Optional but recommended) Create and enter a virtual environment:
sudo pip install virtualenv
virtualenv -p python3 hiertext_env
source ./hiertext_env/bin/activate
Then install the required dependencies using:
cd hiertext
pip install -r requirements.txt
The ground-truth annotations of train and validation sets are stored in
gt/train.jsonl.gz, gt/validation.jsonl.gz respectively. Use the following
command to decompress the two files:
gzip -d gt/*.jsonl.gz
The images are hosted by CVDF. To download them one needs to install AWS CLI and run the following:
aws s3 --no-sign-request cp s3://open-images-dataset/ocr/train.tgz .
aws s3 --no-sign-request cp s3://open-images-dataset/ocr/validation.tgz .
aws s3 --no-sign-request cp s3://open-images-dataset/ocr/test.tgz .
tar -xzvf train.tgz
tar -xzvf validation.tgz
tar -xzvf test.tgz
Run the visualization notebook locally to inspect the data using:
jupyter notebook HierText_Visualization.ipynb
We split the dataset into train (8281 images), validation (1724 images) and
test (1634 images) sets. Users should train their models on train set,
select the best model candidate based on evaluation results on validation set
and finally report the performance on test set.
There are five tasks:
- Word-level
- Word detection (polygon)
- End-to-end
- Line-level
- Line detection (union of words)
- End-to-end
- Paragraph detection (union of words)
Images in HierText are of higher resolution with their long side constrained to 1600 pixels compared to previous datasets based on Open Images that are constrained to 1024 pixels. This results in more legible small text. The filename of each image is its corresponding image ID in the Open Images dataset. All images are stored in JPG format.
The ground-truth has the following format:
{
"info": {
"date": "release date",
"version": "current version"
},
"annotations": [ // List of dictionaries, one for each image.
{
"image_id": "the filename of corresponding image.",
"image_width": image_width, // (int) The image width.
"image_height": image_height, // (int) The image height.
"paragraphs": [ // List of paragraphs.
{
"vertices": [[x1, y1], [x2, y2],...,[xn, yn]], // A loose bounding polygon with absolute values.
"legible": true, // If false, the region defined by `vertices` are considered as do-not-care in paragraph level evaluation.
"lines": [ // List of dictionaries, one for each text line contained in this paragraph. Lines in paragraph may not follow the reading order.
{
"vertices": [[x1, y1], [x2, y2],...,[x4, y4]], // A loose rotated rectangle with absolute values.
"text": "the text content of the entire line",
"legible": true, // A line is legible if and only if all of its words are legible.
"handwritten": false, // True for handwritten text, false for printed text.
"vertical": false, // If true, characters have a vertical layout.
"words": [ // List of dictionaries, one for each word contained in this line. Words inside a line follows the reading order.
{
"vertices": [[x1, y1], [x2, y2],...,[xm, ym]], // Tight bounding polygons. Curved text can have more than 4 vertices.
"text": "the text content of this word",
"legible": true, // If false, the word can't be recognized and the `text` field will be an empty string.
"handwritten": false, // True for handwritten text, false for printed text.
"vertical": false, // If true, characters have a vertical layout.
}, ...
]
}, ...
]
}, ...
]
}, ...
]
}-
Lines in a paragraph may not follow the reading order while words inside a line are ordered respect to the proper reading order.
-
Vertices in the ground-truth word polygon follow a specific order. See the below figure for details.
Uses the following command for word-level detection evaluation:
python3 eval.py --gt=gt/validation.jsonl --result=/path/to/your/results.jsonl --output=/tmp/scores.txt --mask_stride=1
Add --e2e for end-to-end evaluation. Add --eval_lines and
--eval_paragraphs to enable line-level and paragraph-level evaluation.
Word-level evaluation is always performed.
Be careful when you set the mask_stride parameter. Please read the flag's
definition. For results intended to be included in any publications, users are
required to set --mask_stride=1.
To expedite the evaluation, users can also set the num_workers flag to run the
job in parallel. Note that using too many workers may result in OOM.
Your predictions should be in a .jsonl file with the following format, even
for word-level only evaluation, in which case a paragraph can contain a single
line which contains a single word. For detection only evaluation, text can be
set to an empty string.
{
"annotations": [ // List of dictionaries, one for each image.
{
"image_id": "the filename of corresponding image.",
"paragraphs": [ // List of paragraphs.
{
"lines": [ // List of lines.
{
"text": "the text content of the entire line", // Set to empty string for detection-only evaluation.
"words": [ // List of words.
{
"vertices": [[x1, y1], [x2, y2],...,[xm, ym]],
"text": "the text content of this word", // Set to empty string for detection-only evaluation.
}, ...
]
}, ...
]
}, ...
]
}, ...
]
}NOTE In evaluation, lines and paragraphs are defined as the union of pixel-level masks of the underlying word level polygons.
We attached a sample output file in compressed form, sample_output.jsonl.gz,
to this repo. Use gzip -d sample_output.jsonl.gz to uncompress it and pass to
--result. You should be able to see the scores as those in
sample_eval_scores.txt. These are the outputs and results on the validation
set of the Unified Detector (line based) model proposed in our paper. Note the
results are different from the ones reported in the paper which are computed on
the test set.
To evaluate on the test set, please go to the Robust Reading Competition website.
You will need to compress your json file with gzip before uploading it. The evaluation will take around 0.5~1 hour.
The HierText dataset are released under CC BY-SA 4.0 license.
Please cite our paper if you use the dataset in your work:
@inproceedings{long2022towards,
title={Towards End-to-End Unified Scene Text Detection and Layout Analysis},
author={Long, Shangbang and Qin, Siyang and Panteleev, Dmitry and Bissacco, Alessandro and Fujii, Yasuhisa and Raptis, Michalis},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2022}
}
@article{long2023icdar,
title={ICDAR 2023 Competition on Hierarchical Text Detection and Recognition},
author={Long, Shangbang and Qin, Siyang and Panteleev, Dmitry and Bissacco, Alessandro and Fujii, Yasuhisa and Raptis, Michalis},
journal={arXiv preprint arXiv:2305.09750},
year={2023}
}
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