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--- |
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license: mit |
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task_categories: |
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- image-to-text |
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language: |
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- en |
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- hi |
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- te |
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- ta |
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- or |
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- ur |
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- ml |
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- zh |
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- pa |
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- gu |
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- bn |
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pretty_name: MUSTARD |
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size_categories: |
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- 1K<n<10K |
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tags: |
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- Table |
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- TSR |
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- Table Structure |
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- Table Recognition |
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--- |
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# Dataset Card for MUSTARD |
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## Dataset Details |
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### Dataset Description |
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MUSTARD (Multilingual Scanned and Scene Table Structure Recognition Dataset) is a diverse dataset curated for table structure recognition across multiple languages. The dataset consists of tables extracted from magazines, including printed, scanned, and scene-text tables, labeled with Optimized Table Structure Language (OTSL) sequences. It is designed to facilitate research in multilingual table structure recognition, particularly for non-English documents. |
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- **Curated by:** IIT Bombay LEAP OCR Team |
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- **Funded by:** IRCC, IIT Bombay, and MEITY, Government of India |
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- **Shared by:** IIT Bombay LEAP OCR Team |
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- **Language(s) (NLP):** Hindi, Telugu, English, Urdu, Oriya, Malayalam, Assamese, Bengali, Gujarati, Kannada, Punjabi, Tamil, Chinese |
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- **License:** MIT |
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### Dataset Sources |
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- **Repository:** [GitHub Repository](https://github.com/IITB-LEAP-OCR/SPRINT) |
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- **Paper:** [SPRINT: Script-agnostic Structure Recognition in Tables (ICDAR 2024)](https://arxiv.org/abs/2503.11932) |
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- **Dataset Download:** [Hugging Face Link](https://huggingface.co/datasets/badrivishalk/MUSTARD) |
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## Uses |
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### Direct Use |
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MUSTARD is primarily intended for training and evaluating table structure recognition models, especially those dealing with multilingual and script-agnostic document analysis. |
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### Out-of-Scope Use |
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The dataset should not be used for tasks unrelated to table structure recognition. Additionally, any application involving sensitive data extraction should ensure compliance with relevant legal and ethical guidelines. |
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## Dataset Structure |
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The dataset consists of: |
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- **1428 tables** across 13 languages. |
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- Labels provided in **OTSL format**. |
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- A mixture of **printed, scanned, and scene-text tables**. |
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## Dataset Creation |
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### Curation Rationale |
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The dataset was created to address the lack of multilingual table structure recognition resources, enabling research beyond English-centric datasets. |
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### Source Data |
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#### Data Collection and Processing |
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- Tables were sourced from various magazines. |
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- Labeled using **OTSL sequences** to provide a script-agnostic representation. |
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- Ground truth annotations were validated for accuracy. |
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#### Who are the source data producers? |
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The dataset was curated by researchers at IIT Bombay, specializing in OCR and document analysis. |
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### Annotations |
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#### Annotation Process |
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- Tables were manually labeled using **OTSL sequences**. |
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- Verification was performed to ensure consistency. |
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- Annotations were aligned with **HTML-based table representations** for interoperability. |
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#### Who are the annotators? |
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Annotations were performed by research scholars and experts in OCR and document processing at IIT Bombay. |
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#### Personal and Sensitive Information |
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The dataset does not contain personally identifiable or sensitive information. |
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## Bias, Risks, and Limitations |
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- **Bias:** The dataset is derived primarily from magazines, which may not fully represent all document styles. |
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- **Limitations:** The dataset size is limited (1428 tables), and performance may vary on unseen data sources. |
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- **Risks:** Use in sensitive domains should be accompanied by proper validation and legal compliance. |
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### Recommendations |
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Users should be aware of dataset limitations and biases when applying models trained on MUSTARD to other real-world scenarios. |
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## Citation |
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If you use this dataset in your research, please cite it as: |
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``` |
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@InProceedings{10.1007/978-3-031-70549-6_21, |
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author="Kudale, Dhruv and Kasuba, Badri Vishal and Subramanian, Venkatapathy and Chaudhuri, Parag and Ramakrishnan, Ganesh", |
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editor="Barney Smith, Elisa H. and Liwicki, Marcus and Peng, Liangrui", |
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title="SPRINT: Script-agnostic Structure Recognition in Tables", |
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booktitle="Document Analysis and Recognition - ICDAR 2024", |
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year="2024", |
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publisher="Springer Nature Switzerland", |
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address="Cham", |
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pages="350--367", |
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isbn="978-3-031-70549-6", |
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url = "https://arxiv.org/abs/2503.11932" |
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} |
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``` |
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## More Information |
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For further details, refer to: |
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- **SPRINT Model:** [GitHub Repository](https://github.com/IITB-LEAP-OCR/SPRINT) |
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- **Pretrained Models:** [Model Releases](https://github.com/IITB-LEAP-OCR/SPRINT/releases/tag/models) |
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- **Dataset Download:** [Hugging Face Dataset](https://huggingface.co/datasets/badrivishalk/MUSTARD) |
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## Dataset Card Authors |
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- Badri Vishal Kasuba |
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- Dhruv Kudale |
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## Dataset Card Contact |
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For queries, contact the authors via their respective institutional affiliations. |
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## License |
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The dataset is licensed under the **MIT License**, allowing for free use and modification with proper attribution. |
