Search is not available for this dataset
image
imagewidth (px) 1.2k
4.43k
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float64 1.2k
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float64 750
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End of preview. Expand
in Data Studio
Dataset Card for SignverOD
Detecting the presence and location of hand written artifacts such as signatures, dates, initials can be critical for scanned (offline)document processing systems. This capability can support multiple downstream tasks such as signature verification, document tagging and categorization.
SignverOD is a curated dataset of 2576 scanned document images with 7103 bounding box annotations, across 4 categories (signature, initials, redaction, date). SignverOD cover a diverse set of document types including memos, emails, bank cheques, lease agreements and letters, memos, invoices.
Dataset Sources
Original source: https://www.kaggle.com/datasets/victordibia/signverod
Images of documents in this dataset are sourced from 4 main locations and then annotated.
Tobacco800
Tobacco800 is a publicly accessible document image collection with realistic scope and complexity is important to the document image analysis and search community. https://www.kaggle.com/sprytte/tobacco-800-dataset.
NIST.gov Special Database
The NIST.gov structured Forms Database consists of 5,590 pages of binary, black-and-white images of synthesized documents. The documents in this database are 12 different tax forms from the IRS 1040 Package X for the year 1988. https://www.nist.gov/srd/nist-special-database-2
Bank Cheques
The bank cheques dataset is a collection of xx colored images of bank checks. They consist of scanned realistic checks as well as examplar signatures with signatures. https://www.kaggle.com/saifkhichi96/bank-checks-signatures-segmentation-dataset
GSA.gov Lease Documents
GSA provides electronic copies of GSA lease documents for general public viewing. The lease documents are sorted by region and contain, for the most part, GSA Lease Forms and Lease Amendments (LA) from selected GSA leases across the nation. https://www.gsa.gov/real-estate/real-estate-services/leasing/executed-lease-documents
CiItation
If you use this dataset as part of your work or experiments, please consider citing:
@article{DibiaReed2022signverod,
author = {Victor, Dibia},
title = {A Dataset for Handwritten Signature Object Detection in Scanned Documents.},
year = {2022},
publisher = {victordibia.com},
journal = {victordibia.com},
url = {https://victordibia.com/signverod.pdf}
}
@article{Dibia2022signver,
author = {Victor, Dibia and Andrew Reed},
title = {SignVer: A Deep Learning Library for Automatic Offline Signature Verification Tasks},
year = {2022},
publisher = {victordibia.com},
journal = {victordibia.com},
url = {https://victordibia.com/signver.pdf}
}
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