<span>The recognition of Arabic handwritten is received at the same interest as other Latin languages. In Optical Character Recognition (OCR), handwriting Arabic recognition is considered as one of the critical and difficult tasks in the various scientific area. The main issues of this matter were due to the lack of public Arabic handwriting databases and the cursive nature of Arabic writing. In this paper, a new benchmark database is built for the Arabic and English off-line handwritten digits Recognition. The original form is divided into three groups: Arabic digits, English digits, and word Arabic digits which written five times by 100 different academic staff and students of university writers. Our database contains 14500 images; divided into two subsets of training and testing to help researchers through evaluating and comparing results obtained from their systems. </span>
One of the most severe issues in the cloud is security. In comparison to financial data, so it is extremely sensitive and must be safeguarded against unwanted access. We have developed a proposed system based on three different keys. We divided the data into insensitive, sensitive, and highly sensitive data. The data will be saved on a separate cloud server. The proposed system used different keys for encryption and decryption purposes. The elliptic curve cryptography (ECC) based distributed cloud-based secure data storage (DDSPE) approach was proposed to provide secure large data based data protection across the different clouds. With DDSPE technology, the ECC scheme has been used for encryption and decryption purposes. The cloud is used for simulation. The results of the tests reveal that the suggested DDSPE system is safe and saves time regarding data retention and retrieval. To analyze performance, we compared the DDSPE method with advance encryption standard (AES), blowfish, rivest shamir adleman (RSA), security-aware efficient distributed storage (SA-EDS), and attribute based encryption (ABE) based secure distributed storage (ASDSS). In terms of information retention and recovery, our methodology is quite effective because it requires less amount of time as compared to other strategies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.