Arabic Handwritten Characters Recognition Using Convolutional Neural Network release_553wfvnalvdszchomlxf5bn2qi

by Mohamed Loey

Published by figshare.

2020  

Abstract

Handwritten Arabic character recognition systems face several challenges, including the unlimited variation in human handwriting and large public databases. In this work, we model a deep learning architecture that can be effectively apply to recognizing Arabic handwritten characters. A Convolutional Neural Network (CNN) is a special type of feed-forward multilayer trained in supervised mode. The CNN trained and tested our database that contain 16800 of handwritten Arabic characters. In this paper, the optimization methods implemented to increase the performance of CNN. Common machine learning methods usually apply a combination of feature extractor and trainable classifier. The use of CNN leads to significant improvements across different machine-learning classification algorithms. Our proposed CNN is giving an average 5.1% misclassification error on testing data
In text/plain format

Archived Files and Locations

application/pdf  1.3 MB
file_h6ro7drly5czjifeill6xms7ue
s3-eu-west-1.amazonaws.com (publisher)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2020-05-03
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 61a41e33-948e-42ea-8052-6d999c485e32
API URL: JSON