Spatial and Textural Aspects for Arabic Handwritten Characters Recognition

Boulid, Youssef; Souhar, Abdelghani; Ouagague, Mly. M.

doi:10.9781/ijimai.2017.12.002

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…The histograms are then concatenated to create a feature vector, which is fed into a neural network classifier. In the same regard, the paper (15) aimed to create a method that considers both the spatial and textural aspects of isolated Arabic handwritten character recognition. To extract features from the char-acter images, the authors suggested using local binary patterns (LBP) and a modified bitmap sampling technique.…”

Section: Related Workmentioning

confidence: 99%

Toward Innovative Recognition of Handwritten Arabic Characters: A Hybrid Approach with SIFT, BoVW, and SVM classification

Farhaoui,

Rida Fethi,

Zeroual

et al. 2023

Data and Metadata

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The goal of handwriting recognition has been a top priority for those who want to enter data into computer systems for more than thirty years. In several fields, the advent of handwriting recognition technology is highly anticipated. OCR technology has made it possible for computers to recognize characters as visual objects and collect data about their unique characteristics in recent years. In particular, several studies in this field have focused on Arabic writing. The use of machines to examine handwritten papers is the first step in the character identification process. The identification of specific Arabic characters is the main goal of this particular investigation. In computer vision, Arabic character recognition is very important since it's necessary to correctly recognize and classify Arabic letters and characters in manuscripts. In this research, an innovative approach based on identifying Arabic character characteristics using BoVW (bag of visual words) and SIFT (Scale Invariant Feature Transform) features is proposed. These features are clustered using k-means clustering to produce a dictionary. Following that, SVM (Support Vector Machine) is utilized to classify the word images in a visual codebook created using these terms. The proposed approach is an innovative method to deal with the difficulties associated with Arabic hand-writing recognition. The utilization of BoVW and SIFT features is expected to enhance the system's robustness in recognizing and classifying Arabic characters. The proposed approach will be experimentally evaluated using a dataset that includes a variety of Arabic characters written in various styles. The results of this study will offer important new perspectives on the effectiveness and practicality of the approach suggested.

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Section: Related Workmentioning

confidence: 99%

Toward Innovative Recognition of Handwritten Arabic Characters: A Hybrid Approach with SIFT, BoVW, and SVM classification

Farhaoui,

Rida Fethi,

Zeroual

et al. 2023

Data and Metadata

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show abstract

“…A problem that has been widely studied is how to find the characteristic words of a document (e.g., [44][45][46][47]). ese characteristic words can be used, for instance, to implement keywordbased document search.…”

Section: Characteristic Words Of a Documentmentioning

confidence: 99%

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

Hernández

Pérez

Granados

et al. 2019

Scientific Programming

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This paper addresses the problem of automatically customizing the sending of notifications in a nondisturbing way, that is, by using only implicit-feedback. Then, we build a hybrid filter that combines text mining content filtering and collaborative filtering to predict the notifications that are most interesting for each user. The content-based filter clusters notifications to find content with topics for which the user has shown interest. The collaborative filter increases diversity by discovering new topics of interest for the user, because these are of interest to other users with similar concerns. The paper reports the result of measuring the performance of this recommender and includes a validation of the topics-based approach used for content selection. Finally, we demonstrate how the recommender uses implicit-feedback to personalize the content to be delivered to each user.

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“…Spatial and Textural Aspects for Arabic Handwritten Characters Recognition (Morocco), Boulid, et al purpose was the recognition of handwritten Arabic characters in their isolated form, and had 2.82% error rate [11].…”

Section: Eeg Signal Analysis Of Writing and Typing Between Adults Withmentioning

confidence: 99%

Editor’s Note

Khari¹

2018

IJIMAI

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Spatial and Textural Aspects for Arabic Handwritten Characters Recognition

Cited by 7 publications

References 5 publications

Toward Innovative Recognition of Handwritten Arabic Characters: A Hybrid Approach with SIFT, BoVW, and SVM classification

Toward Innovative Recognition of Handwritten Arabic Characters: A Hybrid Approach with SIFT, BoVW, and SVM classification

A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback

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