Abdeljalil Gattal

Verified

Abdeljalil verified their affiliation via an institutional email.

Learn more

Verified

Abdeljalil verified their affiliation via an institutional email.

Learn more

• Professor
• Professor at Université de Tébessa

Personality trait identification through handwriting analysis presents a challenging area within automated document recognition based on Artificial Intelligence solutions. Recent studies relied on solutions automating graphonomic processes, while others address only a few local features, conversely few studies offer solutions based on textural feat...

Script identification is crucial for document analysis and optical character recognition (OCR). This study proposes YafNet, a novel convolutional neural network (CNN) architecture, developed from scratch, to tackle the challenges of script identification in both handwritten and printed word images. YafNet dynamically weights features, enabling it t...

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 ban...

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 machin...

This paper presents an efficient way to use deep convolutional neural networks (CNNs) to improve image classification systems' performance. CNN automatically extracts local and global features from the normalized image. Different convolutional neural network configurations are used for classification, and an experimental study was conducted to asse...

This work describes an efficient method for offline handwriting, Arabic word recognition, and digit recognition. The system employs a global recognition method, wherein segmentation of words is not used and the image is identified as a whole. To create this system, a preprocessing operation is applied to images of words or digits, which includes si...

With the ongoing development of machine learning techniques, it is now necessary to train and evaluate these algorithms to have access to high-quality medical X-ray datasets. This study unfolds on two critical axes within the realm of medical imaging. We introduce the proposed Medical X-ray Imaging Dataset (MXID), a meticulously curated resource fe...

Due to severe deterioration and writing style differences, keyword spotting from historical handwritten documents remains challenging. This paper uses query-by-example (QBE) and a segmentation-based technique to investigate keyword spotting in historical documents. To match the image of the query to those in a reference database, features extracted...

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 g...

Among the numerous methods found in the literature that researchers used to develop intrusion detection systems, Artificial Neural Networks (ANN) were the most used machine learning techniques, which is why they were chosen as the main focus in this study, which we relied on to develop a new model that can detect network anomalies. The training sta...

Machine learning techniques have made significant progress in recent years in the field of healthcare by assisting clinicians in treatment interventions, identification, detection along with the classification of a variety of diseases, including Brain Tumors, Breast Cancer, diagnosis of diabetic retinopathy, as well as, more recently in dealing wit...

Identifying the writer of a handwritten document has remained an interesting pattern classification problem for document examiners, forensic experts, and paleographers. While mature identification systems have been developed for handwriting in contemporary documents, the problem remains challenging from the viewpoint of historical manuscripts. Desi...

Recognition of handwritten digits is a dominant research problem in the field of image analysis and pattern recognition. In particular, feature extraction-based appearance approaches solve many research problems. In this work, we introduce a new method using encryption techniques such as the feature extraction stage. Furthermore, this method is ver...

Over the past years, deep learning techniques has had a strong impact on many areas of technical intelligence, including handwriting recognition. Handwriting recognition it is defined as the domain which allow a computer to interpret intelligible handwritten inputs from different sources. It is plays an important role in many areas, such as authent...

Machine learning techniques have made significant progress in recent years in the field of healthcare by assisting clinicians in treatment interventions, identification, detection along with the classification of a variety of diseases, including Brain Tumors, Breast Cancer, diagnosis of diabetic retinopathy, as well as, more recently in dealing wit...

Spotting keywords in modern handwritten documents is an interesting problem that allows to search, index, and classify document images. This paper investigates the word spotting problem in a segmentation-based framework where features extracted from word images are employed to match a query keyword with those in a reference base. More specifically,...

The paper presents a summary of the 1st Competition on Script Identification in the Wild (SIW 2021) organised in conjunction with 16th International Conference on Document Analysis and Recognition (ICDAR 2021). The goal of SIW is to evaluate the limits of script identification approaches through a large scale in the wild database including 13 scrip...

The paper presents a summary of the 1st Competition on Script Identification in the Wild (SIW 2021) organised in conjunction with 16th International Conference on Document Analysis and Recognition (ICDAR 2021). The goal of SIW is to evaluate the limits of script identification approaches through a large scale in the wild database including 13 scrip...

Handwritten digits recognition is a key research problem in the domain of image analysis and pattern recognition. Specifically, the appearance approaches based on feature extraction have been proposed to solve many research issues. This paper presents a novel way of extending the oriented Basic Image Features column (oBIFs column) to multi-scale fe...

Writer identification from handwriting is still considered to be challenging task due to homogeneous vision comparing writer of handwritten documents. This paper presents a new method based on two LBPs kinds: Complete Local Binary Patterns (CLBP) and Local Binary Pattern Variance (LBPV) for extracting the features from handwriting documents. The fe...

Identification of writers from images of handwriting is an interesting research problem in the handwriting recognition community. Application of image analysis and machine learning techniques to this problem allows development of computerized solutions which can facilitate forensic experts in reducing the search space against a questioned document....

Codebook-based writer characterization is an effective technique that has been investigated in a number of recent studies on identification and verification of writers. These methods divide a set of writing samples into small units (fragments or graphemes) and cluster these patterns to produce a codebook. Writer of a handwritten sample is then char...

Nowadays, personality identification based on handwriting processing is becoming a very active field of research and experimentation, for the reason of its high demand in domains such as resources management, criminal investigations and mental health diagnostics. The implicit information includes attributes like writer identity, gender, age group a...

Ce support de cours est un guide d’initiation à la programmation Web, Ce guide vous permettra de tirer parti de fonctionnalités portées par HTML, Javascript, CSS et Java Script. L’objectif de ce support est de permettre aux étudiants : Apprendre à mettre en oeuvre une application Web ; Apprendre la syntaxe de base des différents principaux langages...

Gender Classification from handwriting is still considered to be challenging due to homogeneous vision comparing male and female handwritten documents. This paper presents a new method based on Cloud of Line Distribution (COLD) and Hinge feature for distinguishing the gender from handwriting. The SVM classifier combination decides the assigned clas...

Offline signature verification has been the most commonly employed modality for authentication of an individual and, it enjoys global acceptance in legal, banking and official documents. Verifying the authenticity of a signature (genuine or forged) remains a challenging problem from the perspective of computerized solutions. This paper presents a s...

Offline signature verification has been the most commonly employed modality for authentication of an individual and, it enjoys global acceptance in legal, banking and official documents. Verifying the authenticity of a signature (genuine or forged) remains a challenging problem from the perspective of computerized solutions. This paper presents a s...

Writer characterization from images of handwriting has remained an important research problem in the handwriting recognition community that finds applications in forensics, paleography and neuropsychology. This paper presents a study to evaluate the effectiveness of an implicit shape codebook technique to recognize writer from digitized images of h...

Several approaches for gender of handwriting are proposed an appearance feature-based approach. In this paper we present a comparative study to evaluate effectiveness of different Local Binary Patterns methodologies in characterizing gender from handwriting. We investigate different local binary patterns (LBP) parameters with/without preprocessing...

The document binarization is a primary processing step toward document recognition system. It goals to separate the foreground from the document background. In this paper, we propose an algorithm for the binarization of document images degraded by using the clustering algorithm K-Means with automatic parameter tuning. It uses the K-Means algorithm...

This paper describes the ICFHR 2018 Competition on Multi-script Writer Identification with details on the competition tasks, databases employed, submitted systems, evaluation protocol and the reported results. The competition was aimed at exploring the traditional writer identification problem in a more challenging scenario of a multi-script enviro...

This study addresses the problem of identifying the authorship of historical manuscripts, a challenging task that offers interesting applications for document examiners and paleographers. We exploit handwriting texture as the discriminative attribute characterizing the writer of a given document. The textural information in handwriting is captured...

Classification of gender from images of handwriting is an interesting research problem in computerized analysis of handwriting. The correlation between handwriting and gender of writer can be exploited to develop intelligent systems to facilitate forensic experts, document examiners, paleographers, psychologists and neurologists. We propose a handw...

Several approaches for handwritten digits recognition are proposed an appearance feature-based approach. In this paper we process handwritten digit image without deskewing using oriented Basic Image Features (oBIF) Column scheme extracted from the complete image as well as from different regions of the image by applying a uniform grid sampling to t...

The segmentation of handwritten digit strings into isolated digits remains a challenging task. The difficulty for recognizing handwritten digit strings is related to several factors such as sloping, overlapping, connecting and unknown length of the digit string. Hence, this paper aims to propose a segmentation and recognition system for unknown-len...

Ce support de cours est un guide d’initiation à Matlab, Matlab permet le travail interactif de calcul scientifique utilisable pour la résolution numérique de nombreux problèmes mathématiques. En outre, Matlab dispose de développement avec l’outil graphique. L’objectif de ce support est de permettre aux étudiants : • Se familiariser rapidement avec...

This competition is aimed at classification of writer demographics from offline handwritten documents using the QUWI database. QUWI is a bilingual database comprising writing samples of same individuals in Arabic and English. This allows evaluating the performance of different systems in a more challenging multi-script environment. This paper prese...

Automatic reading of digit fields from an image document has been proposed in several applications such as bank checks, postal code and forms. In this context, two main problems are occurred when attempting to design a handwritten digit string recognition system. The first problem is the link between adjacent digits, which can be naturally spaced,...

This study demonstrates how the combination of oriented Basic Image Features (oBIFs) with the background concavity features can be effectively employed to enhance the performance of isolated digit recognition systems. The features are extracted without any size normalization from the complete image as well as from different regions of the image by...

This paper investigates a number of verification rules to validate the segmentation of connected handwritten digits. The verification technique based on statistical reasoning and fuzzy integrals is employed to verify the segmentation through decision functions produced by multiclass SVM based recognizers. The segmentation relies on an oriented slid...

This competition targets writer identification and gender classification from offline handwritten documents using the QUWI database. The most interesting aspect of the competition is the use of a dataset with writing samples of the same individual in Arabic as well as English. The competition not only allows an objective comparison of different sys...

We propose in this paper a system to recognize handwritten digit strings, which constitutes a difficult task because of overlapping and/or joining of adjacent digits. To resolve this problem, we use a segmentation-verification of handwritten connected digits based conjointly on the oriented sliding window and support vector machine (SVM) classifier...

This paper investigates the combination of different statistical and structural features for recognition of isolated handwritten digits, a classical pattern recognition problem. The objective of this study is to improve the recognition rates by combining different representations of non-normalized handwritten digits. These features include some glo...

This paper introduces a new offline handwriting database that was developed to be employed in performance evaluation, result comparison and development of new methods related to handwriting analysis and recognition. The database can particularly be used for signature verification, writer recognition and writer demographics classification. In additi...

Biometric identification of persons has mainly been based on fingerprints, face, iris and other similar attributes. We propose a handwriting-based biometric identification system using a large database of Arabic handwritten documents. The system first extracts, from each handwritten sample, a set of features including run lengths, edge-hinge and ed...

In this chapter, the features generation is an interested recognition module in a particular and very serious aspect in the field of automatic recognition of normalized and not normalized isolated handwritten digits, which requires having a great database of digits. As the aim is to improve the performance of our recognition system, we are focused...

In this paper, we interested on a particular aspect and very serious in the domain of automatic recognition of normalized and not normalized isolated handwritten digits, the latter is feature generation, which requires having a large database of digits. Where the objective is to improve the performance of our recognition system, we are focused on t...

In this paper, we propose a system to recognize handwritten digit strings, which constitutes a difficult task because of overlapping and/or joining of adjacent digits. To resolve this problem, we use a segmentation-recognition of handwritten connected digits based on the oriented sliding window. The proposed approach allows separating adjacent digi...

The most important step in a recognition system of the amount bank check is the separation of digits from each other. This step called segmentation remains difficult because of overlapping and / or joining two consecutive digits. To resolve this problem, several segmentation methods have been developed, however, each one having its advantage and di...

The hand written digit segmentation is the most important module for hand written digit recognition, which constitutes a difficult task because of overlapping and / or connected of adjacent digits. To resolve this problem, several segmentation methods have been developed each one having its advantage and disadvantage. In this work, we propose a seg...

Dans un système de reconnaissance numérique d'un chèque bancaire, la phase la plus importante réside dans la séparation des chiffres les uns des autres. Cette phase appelée segmentation reste cependant délicate à cause du chevauchement et/ou l'accolement de deux chiffres consécutifs. Pour résoudre ce problème, plusieurs méthodes de segmentation ont...