Search Results for author: Sharad Joshi

Found 6 papers, 1 papers with code

Source Printer Identification using Printer Specific Pooling of Letter Descriptors

no code implementations23 Sep 2021 Sharad Joshi, Yogesh Kumar Gupta, Nitin Khanna

The digital revolution has replaced the use of printed documents with their digital counterparts.

Empirical Evaluation of PRNU Fingerprint Variation for Mismatched Imaging Pipelines

no code implementations4 Apr 2020 Sharad Joshi, Pawel Korus, Nitin Khanna, Nasir Memon

We assess the variability of PRNU-based camera fingerprints with mismatched imaging pipelines (e. g., different camera ISP or digital darkroom software).

Source Printer Identification from Document Images Acquired using Smartphone

no code implementations27 Mar 2020 Sharad Joshi, Suraj Saxena, Nitin Khanna

Source printer identification provides essential information about the origin and integrity of a printed document in a fast and cost-effective manner.

Document Classification General Classification

First Steps Toward CNN based Source Classification of Document Images Shared Over Messaging App

no code implementations17 Aug 2018 Sharad Joshi, Suraj Saxena, Nitin Khanna

Knowledge of source smartphone corresponding to a document image can be helpful in a variety of applications including copyright infringement, ownership attribution, leak identification and usage restriction.

General Classification

Single Classifier-based Passive System for Source Printer Classification using Local Texture Features

1 code implementation22 Jun 2017 Sharad Joshi, Nitin Khanna

This paper proposes a system for classification of source printer from scanned images of printed documents using all the printed letters simultaneously.

General Classification Optical Character Recognition (OCR)

Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents

no code implementations20 Jun 2017 Hardik Jain, Gaurav Gupta, Sharad Joshi, Nitin Khanna

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.

General Classification

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