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IDStack - The Common Protocol for Document Verification built on Digital Signatures
The use of physical documents is inconvenient and inefficient in today's...
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Source Printer Classification using Printer Specific Local Texture Descriptor
The knowledge of source printer can help in printed text document authen...
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Recovering Homography from Camera Captured Documents using Convolutional Neural Networks
Removing perspective distortion from hand held camera captured document ...
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Fisheye Distortion Rectification from Deep Straight Lines
This paper presents a novel line-aware rectification network (LaRecNet) ...
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Source Printer Identification from Document Images Acquired using Smartphone
Vast volumes of printed documents continue to be used for various import...
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Bag-of-Visual-Words for Signature-Based Multi-Script Document Retrieval
An end-to-end architecture for multi-script document retrieval using han...
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Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents
In this digital era, one thing that still holds the convention is a printed archive. Printed documents find their use in many critical domains such as contract papers, legal tenders and proof of identity documents. As more advanced printing, scanning and image editing techniques are becoming available, forgeries on these legal tenders pose a serious threat. Ability to easily and reliably identify source printer of a printed document can help a lot in reducing this menace. During printing procedure, printer hardware introduces certain distortions in printed characters' locations and shapes which are invisible to naked eyes. These distortions are referred as geometric distortions, their profile (or signature) is generally unique for each printer and can be used for printer classification purpose. This paper proposes a set of features for characterizing text-line-level geometric distortions, referred as geometric distortion signatures and presents a novel system to use them for identification of the origin of a printed document. Detailed experiments performed on a set of thirteen printers demonstrate that the proposed system achieves state of the art performance and gives much higher accuracy under small training size constraint. For four training and six test pages of three different fonts, the proposed method gives 99% classification accuracy.
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