This paper introduces a novel dataset for Arabic handwritten isolated alphabet letters to serve as a benchmark for future research. The study presents a comparative evaluation of window-based descriptors for Arabic handwritten alphabet recognition, testing different descriptors with various classifiers. The experiments demonstrate that window-based descriptors perform well, especially when combined with a novel spatial pyramid partitioning scheme. Why it matters: The new dataset and analysis of descriptors will help advance Arabic OCR and handwritten text recognition systems.
Researchers from Alexandria University introduce AlexU-Word, a new dataset for offline Arabic handwriting recognition. The dataset contains 25,114 samples of 109 unique Arabic words, covering all letter shapes, collected from 907 writers. The dataset is designed for closed-vocabulary word recognition and to support segmented letter recognition-based systems. Why it matters: This dataset can help advance Arabic handwriting recognition systems, addressing a need for high-quality Arabic datasets in NLP research.
This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.
MBZUAI researchers have developed an AI program using vision transformers that can learn a person's handwriting style and generate text in that style. The US Patent and Trademark Office recently granted a patent for this technology, which could aid individuals with writing impairments. The system overcomes limitations of previous GAN-based approaches by processing long-range dependencies in handwriting. Why it matters: This patented AI tool enhances personalized text generation and has potential applications in assistive technology and improving handwriting recognition models.
This paper proposes a machine learning method for early detection and classification of date fruit diseases, which are economically important to countries like Saudi Arabia. The method uses a hybrid feature extraction approach combining L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features. Experiments using a dataset of 871 images achieved the highest average accuracy using Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT) classifiers.
MBZUAI student Fatima Ahmed Khalil Mohamed Alkhoori is researching machine learning techniques to improve traffic sign recognition for autonomous vehicles. Her work focuses on using transformer model architectures to enhance the ability of autonomous vehicles to accurately recognize traffic signs in varying environmental conditions. The research aims to address challenges such as viewing angle, lighting variations, and shadows that can confuse regular models. Why it matters: This research contributes to the advancement of safe and effective autonomous vehicle navigation, aligning with the UAE's vision of having a world-class transportation system.
A proposed recognition system aims to identify missing persons, deceased individuals, and lost objects during the Hajj and Umrah pilgrimages in Saudi Arabia. The system intends to leverage facial recognition and object identification to manage the large crowds expected in the coming decade, estimated to reach 20 million pilgrims. It will be integrated into the CrowdSensing system for crowd estimation, management, and safety.
A public talk announcement features Professor Anil K. Jain from Michigan State University discussing biometric recognition. The talk will cover automated recognition of individuals based on biological and behavioral traits. It will also address challenges, research opportunities, and ongoing projects in Jain's lab related to biometrics. Why it matters: As biometric technologies become increasingly integrated into daily life across the Middle East, understanding their limitations and ethical implications is crucial for responsible development and deployment.