5 datasets found

  1. IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document...

    • zenodo.org
    bin
    Updated Jul 17, 2025
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    Oikantik Nath; Oikantik Nath; Sahithi Kukkala; Sahithi Kukkala; Mitesh Khapra; Mitesh Khapra; Ravi Kiran Sarvadevabhatla; Ravi Kiran Sarvadevabhatla (2025). IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing [Dataset]. http://doi.org/10.5281/zenodo.15881917
    Explore at:
    binAvailable download formats
    Unique identifier
    https://doi.org/10.5281/zenodo.15881917
    Dataset updated
    Jul 17, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Oikantik Nath; Oikantik Nath; Sahithi Kukkala; Sahithi Kukkala; Mitesh Khapra; Mitesh Khapra; Ravi Kiran Sarvadevabhatla; Ravi Kiran Sarvadevabhatla
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    IndicDLP Dataset

    IndicDLP is a large-scale, foundational dataset created to advance document layout parsing in multi-lingual and multi-domain settings. It comprises 119,842 document images covering 11 Indic languages and English: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu. The dataset spans 12 diverse document categories, including Novels, Textbooks, Magazines, Acts & Rules, Research Papers, Manuals, Brochures, Syllabi, Question Papers, Notices, Forms, and Newspapers.

    The dataset contains 42 physical and logical layout classes. IndicDLP includes both digitally-born and scanned documents, with annotations created using Shoonya, an open-source tool built on Label Studio. The dataset is curated to support robust layout understanding across diverse scripts, domains, and document types.

    Project Page : https://indicdlp.github.io/" target="_blank" rel="noopener">IndicDLP

    IndicDLP Model Checkpoints

    We provide 3 model checkpoints — YOLOv10x, DocLayout-YOLO, and RoDLA — finetuned on the IndicDLP dataset. These models are optimized for robust document layout parsing across a wide range of Indic languages and document types, and are capable of detecting all 42 region labels defined in the dataset.

    These checkpoints have demonstrated strong performance on both scanned and digitally-born documents. They are ready to use for inference, serve as strong baselines for benchmarking, and can be further fine-tuned for downstream tasks such as structure extraction or semantic tagging.

  2. k

    Industrial Units, Layouts, Production, Multi Unit Complex, List of Sick...

    • opendata.kp.gov.pk
    Updated Jul 28, 2022
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    (2022). Industrial Units, Layouts, Production, Multi Unit Complex, List of Sick Industries, SOPs in KP [Dataset]. https://opendata.kp.gov.pk/dataset/industrial-units-layouts-production-multi-unit-complex-list-of-sick-industries-sops-in-kp
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    Dataset updated
    Jul 28, 2022
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The dataset contains information regarding the Number of Industrial Units in KP, the Industrial Manufacturing spectrum, and the Census of Manufacturing Industries in KP.

  3. Five experimental evaluation model layouts L1–5.

    • figshare.com
    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Lisa Scholten; Max Maurer; Judit Lienert (2023). Five experimental evaluation model layouts L1–5. [Dataset]. http://doi.org/10.1371/journal.pone.0176663.t002
    Explore at:
    xlsAvailable download formats
    Unique identifier
    https://doi.org/10.1371/journal.pone.0176663.t002
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Lisa Scholten; Max Maurer; Judit Lienert
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Five experimental evaluation model layouts L1–5.

  4. 4

    Data underlying the paper: "Interactive Multi-Constrained...

    • data.4tu.nl
    zip
    Updated Jun 24, 2022
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    Joan le Poole; Hans Hopman; Austin Kana; Etienne Duchateau (2022). Data underlying the paper: "Interactive Multi-Constrained System-to-Compartment Allocation to Support Real-Time Collaborative Complex Ship Layout Design Decision-Making" [Dataset]. http://doi.org/10.4121/20141636.v2
    Explore at:
    zipAvailable download formats
    Unique identifier
    https://doi.org/10.4121/20141636.v2
    Dataset updated
    Jun 24, 2022
    Dataset provided by
    4TU.ResearchData
    Authors
    Joan le Poole; Hans Hopman; Austin Kana; Etienne Duchateau
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    This data set comprises the data underlying the case study in the paper "Interactive Multi-Constrained System-to-Compartment Allocation to Support Real-Time Collaborative Complex Ship Layout Design Decision-Making", to be presented at the International Naval Engineering Conference and Exhibition (INEC 2022) .


    Specifically, the data underlying the case studies is provided. For each case study, the Input is provided, consisting of a set of Systems, System Properties, Interactions, and Compartments. Also, the satisfaction of all design parameters for each developed concept design is provided.

  5. Data from: Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor...

    • zenodo.org
    Updated Nov 9, 2022
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    Aina Nadhirah Nor Hisham; Yin Hoe Ng; Chee Keong Tan; David Chieng; Aina Nadhirah Nor Hisham; Yin Hoe Ng; Chee Keong Tan; David Chieng (2022). Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts [Dataset]. http://doi.org/10.5281/zenodo.7306455
    Explore at:
    Unique identifier
    https://doi.org/10.5281/zenodo.7306455
    Dataset updated
    Nov 9, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Aina Nadhirah Nor Hisham; Yin Hoe Ng; Chee Keong Tan; David Chieng; Aina Nadhirah Nor Hisham; Yin Hoe Ng; Chee Keong Tan; David Chieng
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A detailed description of our dataset can be found here:
    Nor Hisham, A.N.; Ng, Y.H.; Tan, C.K.; Chieng David, Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts. Data 2022, to appear.

    Please cite the paper when using the dataset.

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Oikantik Nath; Oikantik Nath; Sahithi Kukkala; Sahithi Kukkala; Mitesh Khapra; Mitesh Khapra; Ravi Kiran Sarvadevabhatla; Ravi Kiran Sarvadevabhatla (2025). IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing [Dataset]. http://doi.org/10.5281/zenodo.15881917
Organization logo

IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing

Explore at:
binAvailable download formats
Unique identifier
https://doi.org/10.5281/zenodo.15881917
Dataset updated
Jul 17, 2025
Dataset provided by
Zenodohttp://zenodo.org/
Authors
Oikantik Nath; Oikantik Nath; Sahithi Kukkala; Sahithi Kukkala; Mitesh Khapra; Mitesh Khapra; Ravi Kiran Sarvadevabhatla; Ravi Kiran Sarvadevabhatla
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

IndicDLP Dataset

IndicDLP is a large-scale, foundational dataset created to advance document layout parsing in multi-lingual and multi-domain settings. It comprises 119,842 document images covering 11 Indic languages and English: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu. The dataset spans 12 diverse document categories, including Novels, Textbooks, Magazines, Acts & Rules, Research Papers, Manuals, Brochures, Syllabi, Question Papers, Notices, Forms, and Newspapers.

The dataset contains 42 physical and logical layout classes. IndicDLP includes both digitally-born and scanned documents, with annotations created using Shoonya, an open-source tool built on Label Studio. The dataset is curated to support robust layout understanding across diverse scripts, domains, and document types.

Project Page : https://indicdlp.github.io/" target="_blank" rel="noopener">IndicDLP

IndicDLP Model Checkpoints

We provide 3 model checkpoints — YOLOv10x, DocLayout-YOLO, and RoDLA — finetuned on the IndicDLP dataset. These models are optimized for robust document layout parsing across a wide range of Indic languages and document types, and are capable of detecting all 42 region labels defined in the dataset.

These checkpoints have demonstrated strong performance on both scanned and digitally-born documents. They are ready to use for inference, serve as strong baselines for benchmarking, and can be further fine-tuned for downstream tasks such as structure extraction or semantic tagging.

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