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HYBRID ANN AND FIREWORKS ALGORITHM FOR REAL-TIME SOFTWARE RELIABILITY PREDICTION

Ivy Botchway Felix Larbi Aryeh Boniface Kayode Alese
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Hybrid Intelligence: Filtering and Deep Learning for Handwritten Text Recognition

by Arshad Iqbal, Shabbir Hassan, Maria Qamar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 55
Year of Publication: 2024
Authors: Arshad Iqbal, Shabbir Hassan, Maria Qamar
10.5120/ijca2024924273

Arshad Iqbal, Shabbir Hassan, Maria Qamar . Hybrid Intelligence: Filtering and Deep Learning for Handwritten Text Recognition. International Journal of Computer Applications. 186, 55 ( Dec 2024), 20-31. DOI=10.5120/ijca2024924273

@article{ 10.5120/ijca2024924273,
author = { Arshad Iqbal, Shabbir Hassan, Maria Qamar },
title = { Hybrid Intelligence: Filtering and Deep Learning for Handwritten Text Recognition },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2024 },
volume = { 186 },
number = { 55 },
month = { Dec },
year = { 2024 },
issn = { 0975-8887 },
pages = { 20-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number55/hybrid-intelligence-filtering-and-deep-learning-for-handwritten-text-recognition/ },
doi = { 10.5120/ijca2024924273 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-27T02:45:45.139842+05:30
%A Arshad Iqbal
%A Shabbir Hassan
%A Maria Qamar
%T Hybrid Intelligence: Filtering and Deep Learning for Handwritten Text Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 55
%P 20-31
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Handwritten text recognition systems have gained substantial attention in pattern recognition and artificial intelligence due to their applications in digitizing historical documents and automated reading tasks. This study proposes a novel preprocessing approach for recognizing Kufic manuscripts, aiming to improve the accuracy of handwritten text segmentation. The approach consists of three stages: noise removal, thresholding, and additional noise removal after thresholding. Hybrid filtering, using Wiener and Median filters, effectively reduces noise in the input images. The u-Net deep learning model is employed for precise thresholding and segmentation of the handwritten text. The post-thresholding noise removal step refines the segmented regions and eliminates residual noise artifacts. The proposed approach contributes to enhancing the Quranic Kufic Manuscripts Recognition System (QKMRS) and advancing the digitization of historical documents and the preservation of cultural heritage. The performance measures for noise removal include Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR). For segmentation and thresholding Intersection over union (IoU), entropy and dice coefficient are used.

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Index Terms

Computer Science
Information Sciences

Keywords

Filtering Kufic Manuscript Median Filter Thresholding U-net Wiener Filter.

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