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May 17, 2026International Journal of Cloud Computing

Optimizing Text Detection and Recognition in Video with an Improved Deep Residual Network

Laxmikant Eshwarappa N.A.

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Key Points

The aim is to enhance text detection and recognition in video through optimized deep learning techniques.
Utilized an improved deep residual network architecture for text detection and recognition.
Evaluated performance using various video datasets to assess the effectiveness of the approach.
Conducted comparative analyses against baseline models in terms of accuracy and processing speed.
Achieved a 15% increase in text recognition accuracy (95% CI 0.12-0.18, P<0.01) compared to baseline models.
Reduced processing time by 20% for real-time video analysis, highlighting improved efficiency.

Implication

Study optimizes text detection and recognition in video, suggesting significant improvements in processing efficiency.

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Cite This Study

Laxmikant Eshwarappa N.A. (2026) studied this question.

synapsesocial.com/papers/6a0bfe08166b51b53d37942c https://doi.org/10.1504/ijcc.2027.10078469

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