Abdelwahed Motwakel^1,*, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Ayman Yafoz⁴, Mahmoud Othman⁵, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1387-1403, 2024, DOI:10.32604/csse.2023.033902 - 13 September 2024

Abstract Handwritten character recognition becomes one of the challenging research matters. More studies were presented for recognizing letters of various languages. The availability of Arabic handwritten characters databases was confined. Almost a quarter of a billion people worldwide write and speak Arabic. More historical books and files indicate a vital data set for many Arab nations written in Arabic. Recently, Arabic handwritten character recognition (AHCR) has grabbed the attention and has become a difficult topic for pattern recognition and computer vision (CV). Therefore, this study develops fireworks optimization with the deep learning-based AHCR (FWODL-AHCR) technique. The… More >

Abida Aziz¹, Shafiqa Bano¹, Mubshar Hussain², Muhammad Farooq Azhar³, Ghulam Yasin³, Naila Hadayat⁴, Iqra Arooj⁵, Abeer Hashem⁶, Ajay Kumar⁷, Elsayed Fathi Abd_Allah⁸, Qamar uz Zaman^9,10,*

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1647-1665, 2024, DOI:10.32604/phyton.2024.050974 - 30 July 2024

Abstract Wheat (Triticum aestivum L.) exhibits a greater capacity for cadmium (Cd) absorption compared to other cereal crops, leading to elevated daily Cd intake, and posing a significant threat to public health. For the mitigation of Cd stress in sustainable and environmentally friendly way, a pot study was designed by using exogenous application of various biostimulants, i.e., Nigella sativa and Ocimum sanctum extracts: 0%, 10%, and 20% in combination with the chelating agent ethylenediaminetetraacetic acid (EDTA) using 0 and 5 mg kg under various levels of Cd stress (i.e., 0, 5, 10, and 15 mg kg soil). Results revealed… More > Graphic Abstract

Adéla Hamplová^1,*, Alexey Lyavdansky^2,*, Tomáš Novák¹, Ondřej Svojše¹, David Franc¹, Arnošt Veselý¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2869-2889, 2024, DOI:10.32604/cmes.2024.050791 - 08 July 2024

Abstract This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions, employing two state-of-the-art deep learning algorithms, namely YOLOv8 and Roboflow 3.0. The goal is to contribute to the preservation and understanding of historical texts, showcasing the potential of modern deep learning methods in archaeological research. Our research culminates in several key findings and scientific contributions. We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context. We also created… More >

Salah Alghyaline^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4905-4924, 2024, DOI:10.32604/cmc.2024.052016 - 20 June 2024

Abstract Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles. Arabic is morphologically rich, and its characters have a high similarity. The Arabic language includes 28 characters. Each character has up to four shapes according to its location in the word (at the beginning, middle, end, and isolated). This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters. The proposed architectures were derived from the popular CNN architectures, such as VGG, ResNet, and Inception, to make them applicable to recognizing character-size images. The experimental results on three More >

Sardar Hasen Ali^*, Maiwan Bahjat Abdulrazzaq

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 429-448, 2024, DOI:10.32604/cmc.2024.048356 - 25 April 2024

Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits,… More >

Yuejie Li^1,2,*, Shijun Li³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3041-3070, 2024, DOI:10.32604/cmc.2023.045741 - 26 March 2024

Abstract 6G is envisioned as the next generation of wireless communication technology, promising unprecedented data speeds, ultra-low Latency, and ubiquitous Connectivity. In tandem with these advancements, blockchain technology is leveraged to enhance computer vision applications’ security, trustworthiness, and transparency. With the widespread use of mobile devices equipped with cameras, the ability to capture and recognize Chinese characters in natural scenes has become increasingly important. Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount, such as facial recognition or personal healthcare monitoring. Users can control their visual data and grant or revoke access as needed.… More >

Haipeng Sun^1,2, Xueyan Ding^1,2,*, Jian Sun^1,2, Hua Yu³, Jianxin Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2721-2739, 2024, DOI:10.32604/cmc.2024.046449 - 27 February 2024

Abstract Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition, we present a deep learning-based approach for Yi character detection and recognition. In the detection stage, an improved Differentiable Binarization Network (DBNet) framework is introduced to detect Yi characters, in which the Omni-dimensional Dynamic Convolution (ODConv) is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features, thereby improving the accuracy of Yi character detection. Then, the feature pyramid network fusion module is used to further extract Yi character… More >

Arfa Siddiqu, Abdul Basit^*, Waheed Noor, Muhammad Asfandyar Khan, M. Saeed H. Kakar, Azam Khan

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 875-885, 2023, DOI:10.32604/iasc.2023.036426 - 29 April 2023

Abstract The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages. Moreover, the absence of a standard publicly available dataset for several low-resource languages, including the Pashto language remained a hurdle in the advancement of language processing. Realizing that, a clean dataset is the fundamental and core requirement of character recognition, this research begins with dataset generation and aims at a system capable of complete language understanding. Keeping in view the complete and full… More >

A. Naresh Kumar^1,*, G. Geetha²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2619-2637, 2023, DOI:10.32604/iasc.2023.028349 - 17 August 2022

Abstract Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools. This stipulation make the dispensation period overriding, difficult and tiresome to calculate. This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA). It identifies the prehistoric language, signs and fonts. It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance. For adaptively determining these weights, this paper applies various More >

Rujian Lan^1,2,3,#, Meiyang Duan^1,2,3,#, Feida Wu^1,2,3,#, Rifang Lai^1,2,3, Zhaowen Mo^1,2,3, Shenggang Pan^1,2,3, Xiangru Tang^1,2,3,*

Phyton-International Journal of Experimental Botany, Vol.91, No.11, pp. 2461-2473, 2022, DOI:10.32604/phyton.2022.021884 - 12 July 2022

Abstract Fragrant rice is popular for the good grain quality and special aroma. The present study conducted a field experiment to investigate the effects of ultrasonic seed treatment on grain yield, quality characters, physiological properties and aroma biosynthesis of different fragrant rice genotypes. The seeds of three fragrant rice genotypes were exposed to 1 min of ultrasonic vibration and then cultivated in paddy field. The results of present study showed that ultrasonic seed treatment increased grain yield of all fragrant rice genotypes but the responses of yield formation to ultrasonic were varied with different genotypes. Compared with More >