An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique

Scanned Document Authentication (SDA) is one of the major areas in authenticating document copies, such as tickets, passport pages, or certificates based on the document fingerprints. These fingerprints are represented by the textual features extracted from the scanned documents. Existing SDA method...

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Main Author:	Khaleefah Al-Dulaimi, Shihab Hamad
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	T61-173 Technical education Technical schools
Online Access:	http://eprints.uthm.edu.my/8485/1/24p%20SHIHAB%20HAMAD%20KHALEEFAH%20AL-DULAIMI.pdf http://eprints.uthm.edu.my/8485/2/SHIHAB%20HAMAD%20KHALEEFAH%20AL-DULAIMI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8485/3/SHIHAB%20HAMAD%20KHALEEFAH%20AL-DULAIMI%20WATERMARK.pdf
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spelling	my-uthm-ep.84852023-04-02T00:52:58Z An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique 2022-07 Khaleefah Al-Dulaimi, Shihab Hamad T61-173 Technical education. Technical schools Scanned Document Authentication (SDA) is one of the major areas in authenticating document copies, such as tickets, passport pages, or certificates based on the document fingerprints. These fingerprints are represented by the textual features extracted from the scanned documents. Existing SDA methods mainly use local feature descriptors like local binary patterns and global feature descriptors like Gabor filters. However, the selection of these descriptors and their parameters’ configuration is dependent on the document type and the deformations due to the effects of printing, scanning, usage, and time. To address the dynamic nature of the document deformation, this thesis proposes a new Automated Scanned Document Authentication (ASDA) model that operate based on Collaborative Reinforcement Learning Agent (CRLA) architecture. The proposed ASDA model consists of four feature descriptor agents running on three phases, which are perception, decision, and action to control the operational behaviour of the feature extraction operators. ASDA performs feature matching using the Euclidean Distance and adjusts the feature descriptor agents and their parameters’ configuration according to the matching results. Meanwhile, the CRLA architecture implements a Q-learning strategy to direct the dynamic selection decision of the agents toward the optimum combinations of descriptors and configurations. The selection decision either leads to rewarded or penalty that guide the learning process of the agents with the goal to maximize the rewards results. The performance of the proposed ASDA model is evaluated using the UTHM-EDD and UKM-SPF datasets, which contains shear, lighting (reflection and transmission), crumpling, and printing deformation in three different resolutions of 50, 100, and 150 dpi. The ASDA model achieved an average accuracy of 98.40% for varying deformation conditions in the UTHM-EDD dataset and 100% in the UKM-SPF dataset as compared to other models; ELM, KNN, and OSELM. The results concluded that ASDA is capable to authenticate digital documents under varying degree of deformations with high accuracy. 2022-07 Thesis http://eprints.uthm.edu.my/8485/ http://eprints.uthm.edu.my/8485/1/24p%20SHIHAB%20HAMAD%20KHALEEFAH%20AL-DULAIMI.pdf text en public http://eprints.uthm.edu.my/8485/2/SHIHAB%20HAMAD%20KHALEEFAH%20AL-DULAIMI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/8485/3/SHIHAB%20HAMAD%20KHALEEFAH%20AL-DULAIMI%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	T61-173 Technical education Technical schools
spellingShingle	T61-173 Technical education Technical schools Khaleefah Al-Dulaimi, Shihab Hamad An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique
description	Scanned Document Authentication (SDA) is one of the major areas in authenticating document copies, such as tickets, passport pages, or certificates based on the document fingerprints. These fingerprints are represented by the textual features extracted from the scanned documents. Existing SDA methods mainly use local feature descriptors like local binary patterns and global feature descriptors like Gabor filters. However, the selection of these descriptors and their parameters’ configuration is dependent on the document type and the deformations due to the effects of printing, scanning, usage, and time. To address the dynamic nature of the document deformation, this thesis proposes a new Automated Scanned Document Authentication (ASDA) model that operate based on Collaborative Reinforcement Learning Agent (CRLA) architecture. The proposed ASDA model consists of four feature descriptor agents running on three phases, which are perception, decision, and action to control the operational behaviour of the feature extraction operators. ASDA performs feature matching using the Euclidean Distance and adjusts the feature descriptor agents and their parameters’ configuration according to the matching results. Meanwhile, the CRLA architecture implements a Q-learning strategy to direct the dynamic selection decision of the agents toward the optimum combinations of descriptors and configurations. The selection decision either leads to rewarded or penalty that guide the learning process of the agents with the goal to maximize the rewards results. The performance of the proposed ASDA model is evaluated using the UTHM-EDD and UKM-SPF datasets, which contains shear, lighting (reflection and transmission), crumpling, and printing deformation in three different resolutions of 50, 100, and 150 dpi. The ASDA model achieved an average accuracy of 98.40% for varying deformation conditions in the UTHM-EDD dataset and 100% in the UKM-SPF dataset as compared to other models; ELM, KNN, and OSELM. The results concluded that ASDA is capable to authenticate digital documents under varying degree of deformations with high accuracy.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Khaleefah Al-Dulaimi, Shihab Hamad
author_facet	Khaleefah Al-Dulaimi, Shihab Hamad
author_sort	Khaleefah Al-Dulaimi, Shihab Hamad
title	An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique
title_short	An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique
title_full	An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique
title_fullStr	An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique
title_full_unstemmed	An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique
title_sort	improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2022
url	http://eprints.uthm.edu.my/8485/1/24p%20SHIHAB%20HAMAD%20KHALEEFAH%20AL-DULAIMI.pdf http://eprints.uthm.edu.my/8485/2/SHIHAB%20HAMAD%20KHALEEFAH%20AL-DULAIMI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8485/3/SHIHAB%20HAMAD%20KHALEEFAH%20AL-DULAIMI%20WATERMARK.pdf
_version_	1776103357075685376

An improved multi-agent system for scanned document authentication using collaborative reinforcement learning technique

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