Analytical based approaches in Optical Character Recognition (OCR) systems can endure a significant amount of segmentation errors, especially when dealing with cursive languages such as the Arabic language with frequent overlapping between characters. Holistic based approaches that consider whole words as single units were introduced as an effective approach to avoid such segmentation errors. Still the main challenge for these approaches is their computation complexity, especially when dealing with large vocabulary applications. In this paper, we introduce a computationally efficient, holistic Arabic OCR system. A lexicon reduction approach based on clustering similar shaped words is used to reduce recognition time. Using global word level Discrete Cosine Transform (DCT) based features in combination with local block based features, our proposed approach managed to generalize for new font sizes that were not included in the training data. Evaluation results for the approach using different test sets from modern and historical Arabic books are promising compared with state of art Arabic OCR systems.

This proposed FlowingLife framework addresses the challenges of optimizing altered flow regimes in Irish plans and programs to improve Irish aquatic ecosystems' economic and environmental outcomes. The framework uses Artificial Intelligence (AI) techniques to revolutionize flow regime management and decision-making processing, providing sustainable resource allocation, climate change adaptation, and aquatic habitat conservation. The potential of identifying optimization guides thoroughly evaluating Irish Plans, including development plans, river basin management, biodiversity, and climate action. Fish population restoration, protection of biodiversity, optimization of agricultural techniques, and management of water resources are some of the critical uses. AI-empowered FlowingLife framework creates real-time monitoring and assessment in Strategic Environmental Assessments (SEAs), enabling adaptive management. The FlowingLife evaluates and adaptively manages fish populations and flow regimes by combining Deep Learning (DL) for image and sensor analysis, knowledge graphs for intricating ecological linkages, and predictive modeling. The results show that the proposed paradigm using AI improves environmental management and supports evidence-based decision-making, sustainable resource management, and the preservation of Irish aquatic ecosystems.