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--- |
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license: agpl-3.0 |
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language: |
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- vie |
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pretty_name: Vintext |
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task_categories: |
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- optical-character-recognition |
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tags: |
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- optical-character-recognition |
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--- |
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Vintext is a challenging scene text dataset for Vietnamese, where some characters are equivocal in the visual form due to accent symbols. |
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This dataset contains 2000 fully annotated images with 56,084 text instances. Each text instance is delineated by a quadrilateral bounding box and associated with the ground truth sequence of characters. |
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The dataset is randomly split into three subsets for training (1,200 images), validation (300 images), and testing (500 images). |
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## Languages |
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vie |
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## Supported Tasks |
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Optical Character Recognition |
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## Dataset Usage |
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### Using `datasets` library |
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``` |
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from datasets import load_dataset |
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dset = datasets.load_dataset("SEACrowd/vintext", trust_remote_code=True) |
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``` |
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### Using `seacrowd` library |
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```import seacrowd as sc |
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# Load the dataset using the default config |
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dset = sc.load_dataset("vintext", schema="seacrowd") |
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# Check all available subsets (config names) of the dataset |
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print(sc.available_config_names("vintext")) |
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# Load the dataset using a specific config |
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dset = sc.load_dataset_by_config_name(config_name="<config_name>") |
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``` |
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More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use). |
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## Dataset Homepage |
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[https://github.com/VinAIResearch/dict-guided](https://github.com/VinAIResearch/dict-guided) |
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## Dataset Version |
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Source: 1.0.0. SEACrowd: 2024.06.20. |
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## Dataset License |
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GNU Affero General Public License v3.0 (agpl-3.0) |
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## Citation |
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If you are using the **Vintext** dataloader in your work, please cite the following: |
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``` |
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@INPROCEEDINGS{vintext, |
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author={Nguyen, Nguyen and Nguyen, Thu and Tran, Vinh and Tran, Minh-Triet and Ngo, Thanh Duc and Huu Nguyen, Thien and Hoai, Minh}, |
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booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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title={Dictionary-guided Scene Text Recognition}, |
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year={2021}, |
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pages={7379-7388}, |
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keywords={Training;Visualization;Computer vision;Casting;Dictionaries;Codes;Text recognition}, |
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doi={10.1109/CVPR46437.2021.00730} |
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} |
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@article{lovenia2024seacrowd, |
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title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, |
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author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, |
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year={2024}, |
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eprint={2406.10118}, |
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journal={arXiv preprint arXiv: 2406.10118} |
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} |
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``` |
