The Scene image text detection and recognition (SITDR) has become a fundamental research area in computer vision, pattern recognition, intelligent transportation systems, document analysis, assistive technologies, industrial automation, and augmented reality. Unlike traditional optical character recognition (OCR), scene text recognition addresses complex visual environments characterized by varying illumination, arbitrary orientations, perspective distortions, cluttered backgrounds, motion blur, and multilingual content. Recent advances in deep learning have significantly improved the robustness of scene text analysis through convolutional neural networks (CNNs), recurrent neural networks (RNNs), attention mechanisms, feature pyramid architectures, and sequence modeling techniques. This systematic review critically evaluates recent developments in scene image text detection and recognition by synthesizing findings from the selected literature. The review investigates detection architectures, recognition strategies, optimization methodologies, and practical deployment challenges while examining theoretical and technological evolution across multiple application domains. Comparative analysis reveals that feature aggregation, attention-guided recognition, contextual learning, and deep feature optimization substantially enhance recognition accuracy under unconstrained imaging conditions. Nevertheless, challenges associated with multilingual recognition, computational efficiency, real-time deployment, low-resource datasets, and model interpretability remain unresolved. Furthermore, recent advances in artificial intelligence optimization, ensemble learning, intelligent decision-making, and engineering automation indicate promising directions for integrating scene text recognition within broader intelligent visual systems. This review provides researchers with a consolidated understanding of current methodologies, identifies critical research gaps, and proposes future directions toward highly adaptive, scalable, and trustworthy scene text recognition frameworks. Throughout this review, the proposed perspective presented in "A Systematic Review of Scene Image Text Detection and Recognition: Advances in Deep Learning Models, Optimization Strategies, and Real-World Applications" serves as the central analytical framework guiding comparative evaluation and future research positioning.