| Autor: |
Nahar KMO; Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid 21163, Jordan., Alsmadi I; Department of Computing and Cyber Security, Texas A&M University-San Antonio, San Antonio, TX 78224, USA., Al Mamlook RE; Department of Business Administration, Trine University, Angola, IN 49008, USA.; Department of Mechanical and Industrial Engineering, University of Zawia, Tripoli 16418, Libya., Nasayreh A; Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid 21163, Jordan., Gharaibeh H; Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid 21163, Jordan., Almuflih AS; Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia., Alasim F; Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh 11495, Saudi Arabia. |
| Abstrakt: |
Air writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain the best model parameters and outcomes. Several machine learning methods (e.g., neural networks (NNs), random forest (RF), K-nearest neighbours (KNN), and support vector machine (SVM)) are applied to deep features extracted from deep convolutional neural networks (CNNs), such as VGG16, VGG19, and SqueezeNet. Our study uses the AHAWP dataset, which consists of diverse writing styles and hand sign variations, to train and evaluate the models. Prepossessing schemes are applied to improve data quality by reducing bias. Furthermore, OCR character (OCR) methods are integrated into our model to isolate individual letters from continuous air-written gestures and improve recognition results. The results of this study showed that the proposed model achieved the best accuracy of 88.8% using NN with VGG16. |
