Show simple item record

dc.contributor.authorIslam, Ammad Ul
dc.contributor.authorKhan, Muhammad Jaleed
dc.contributor.authorAsad, Muhammad
dc.contributor.authorKhan, Haris Ahmad
dc.contributor.authorKhurshid, Khurram
dc.date.accessioned2024-09-10T13:05:52Z
dc.date.available2024-09-10T13:05:52Z
dc.date.issued2022-02-16
dc.identifier.pmid35242944
dc.identifier.doi10.1016/j.dib.2022.107964
dc.identifier.urihttp://hdl.handle.net/10147/642820
dc.descriptionThis article presents a dataset of hyperspectral images of handwriting samples collected from 54 individuals. The purpose of the presented dataset is to further explore the use of hyperspectral imaging in document image analysis and to benchmark the performance of forensic analysis methods for hyperspectral document images. Each hyperspectral cube in the dataset has a spatial resolution of 512 × 650 pixels and contains 149 spectral channels in the spectral range of 478-901 nm. All the individuals have different personalities and have their writing patterns. The information of age and gender of each individual is collected. Each subject has written twenty-eight sentences using 12 different varieties of pens from different brands in blue color, each approximately 9 words or 33 characters long, all English alphabets in capital and small cases, digits from 0 to 9. The previous methods use synthetic mixed samples created by joining different parts of the images from the UWA WIHSI dataset.Each document consists of real mixed samples written withdifferent pens and by different writers with a variety of mixing ratios of inks and writers for forensic analysis.The standard A4 pages, each weighing 70 gs and manufactured by "AA" company, are used for data collection. The handwritten notes written by each subject with different pens are annotated in rectangular boxes. This dataset can be used for several tasks related to hyperspectral document image analysis and document forensic analysis including, handwritten optical character recognition, ink mismatch detection, writer identification at sentence, word, and character-level, handwriting-based gender classification, handwriting-based age prediction, handwritten word segmentation, and word generation. This dataset was designed and collected by the research team at the Artificial intelligence and Computer Vision Lab (iVision), Institute of Space Technology, Pakistan, and the hyperspectral images were acquired through imaging spectroscopy in the visible wavelength range at Wageningen University & Research, the Netherlands.en_US
dc.language.isoenen_US
dc.rights© 2022 The Author(s). Published by Elsevier Inc.
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAge estimationen_US
dc.subjectDocument forensicsen_US
dc.subjectDocument image analysisen_US
dc.subjectHandwritten optical character recognitionen_US
dc.subjectHyperspectral image analysisen_US
dc.subjectHyperspectral imagingen_US
dc.subjectInk mismatch detectionen_US
dc.subjectWriter identificationen_US
dc.titleiVision HHID: Handwritten hyperspectral images dataset for benchmarking hyperspectral imaging-based document forensic analysis.en_US
dc.typeArticleen_US
dc.identifier.eissn2352-3409
dc.identifier.journalData in briefen_US
dc.source.journaltitleData in brief
dc.source.volume41
dc.source.beginpage107964
dc.source.endpage
refterms.dateFOA2024-09-10T13:05:54Z
dc.source.countryNetherlands


Files in this item

Thumbnail
Name:
Publisher version
Thumbnail
Name:
main.pdf
Size:
1.957Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

© 2022 The Author(s). Published by Elsevier Inc.
Except where otherwise noted, this item's license is described as © 2022 The Author(s). Published by Elsevier Inc.