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@article{190481,
        author = {Dr.M.Kannadhasan},
        title = {MACHINE VISION AS ENVIRONMENTAL NARRATOR: A STUDY OF SEEING AI AND GOOGLE LOOKOUT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {3063-3068},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190481},
        abstract = {Recent advances in computer vision have transformed assistive technologies for visually impaired users by enabling real-time narration of surrounding environments. Applications such as Seeing AI and Google Lookout employ machine vision and natural language generation to identify objects, people, text, currency, and spatial relationships, thereby translating visual data into spoken descriptions. This paper examines how these applications function as environmental narrators, mediating human–environment interaction through algorithmic perception. By analysing their scene description capabilities, narrative structures, and representational choices, the study explores how machine vision constructs meaning from everyday environments.
Using a qualitative comparative methodology, the paper evaluates selected features of Seeing AI and Google Lookout, including object recognition, scene coherence, temporal immediacy, and contextual framing. Particular attention is given to how these systems prioritize certain elements within complex scenes and how such prioritization shapes users’ understanding of space, activity, and social presence. The analysis reveals that assistive AI does not merely translate visual information but actively curates’ environmental narratives based on data training, probabilistic inference, and linguistic conventions.
The paper argues that these AI-generated narratives reconfigure traditional notions of perception by positioning machines as interpretive intermediaries rather than passive tools. While these technologies significantly enhance accessibility and autonomy, they also raise critical questions regarding accuracy, bias, environmental reductionism, and ethical representation.},
        keywords = {Assistive Artificial Intelligence, Computer Vision, Environmental Narratives, Machine Perception, Accessibility Technologies, Scene Description, Visual-to-Verbal Translation},
        month = {January},
        }