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All Journal Bulletin of Electrical Engineering and Informatics
Gattal, Abdeljalil
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Electrical & Electronics Engineering

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Local binary pattern and its derivatives to handwriting-based gender classification Abbas, Faycel; Gattal, Abdeljalil; Menassel, Rafik
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5488

Abstract

Several studies by psychologists and computer scientists have verified the link between handwriting and writer gender. The texture of the writing image is a major indicator of whether it is male or female writing. This paper conducts a comparison analysis to examine the effectiveness of various local binary patterns (LBPs) techniques in detecting gender from scanned images of handwriting. We study different LBP variants, including complete local binary pattern (CLBP), local ternary pattern (LTP), local configuration pattern (LCP), rotated local binary pattern (RLBP), local binary pattern variance (LBPV), and multi-scale local binary pattern (MLBP), as features for representing handwriting images. A support vector machine (SVM) is trained using features from male and female writing. The method achieves encouraging classification rates of 76.68 when tested on subsets of the Qatar University writer identification (QUWI) dataset containing English and Arabic writing samples when using the experimental protocols of the International Conference on Document Analysis and Recognition (ICDAR) 2013 gender classification competitions.
Deblurring image compression algorithm using deep convolutional neural network Menassel, Rafik; Gattal, Abdeljalil; Kerdoud, Fateh
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7783

Abstract

There are instances where image compression becomes necessary; however, the use of lossy compression techniques often results in visual artifacts. These artifacts typically remove high-frequency detail and may introduce noise or small image structures. To mitigate the impact of compression on image perception, various technologies, including machine learning and optimization metaheuristics that optimize the parameters of image compression algorithms, have been developed. This paper investigates the application of convolutional neural networks (CNNs) to reduce artifacts associated with image compression, and it presents a proposed method termed deblurring compression image using a CNN (DCI-CNN). Trained on a UTKFace dataset and tested on six benchmark images, the DCI-CNN aims to address artifacts such as block artifacts, ringing artifacts, blurring artifacts, color bleeding, and mosquito noise. The DCI-CNN application is designed to enhance the visual quality and fidelity of compressed images, offering a more detailed output compared to generic and other deep learning-based deblurring methods found in related work.
Handwritten digit recognition using a column scheme-based local directional number pattern Aouine, Mohammed; Gattal, Abdeljalil; Djeddi, Chawki; Abbas, Faycel
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7906

Abstract

One of the most well-known challenges in computer vision and machine learning is the recognition of handwritten digits. This study presents an advanced approach to improving isolated-digit recognition through the use of advanced feature extraction techniques. For example, digit recognition is commonly used to read numbers on forms and checks in banks. This paper introduces a novel method of extending the local directional number pattern (LDNP) to a column scheme using two different masks and their resolutions. A new descriptor of the LDNP column scheme is being proposed that combines derivative Gaussian and Kirsch masks in order to enhance textural analysis and capture more detailed local textual information. This approach is highly efficient and robust, able to handle variations in size, shape, and slant. Additionally, the support vector machine (SVM) is employed as a classifier, which has been shown to make better decisions. The empirical investigation is carried out using the CVL dataset, resulting in recognition rates that are comparable with the latest advancements in the field. The overall precision of 96.64% is achieved, outperforming existing similar works.
Co-Authors Abbas, Faycel Aouine, Mohammed Djeddi, Chawki Kerdoud, Fateh Menassel, Rafik