DeepAd-OCR: An AI-Powered Framework for Automated Recognition and Enhancement of Conversion Elements in Digital Advertisements
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Abstract
Digital advertisements represent a critical business asset requiring continuous optimization to maximize conversion rates. While traditional approaches rely on manual analysis and heuristic adjustments, this paper introduces DeepAd-OCR, an AI-driven framework for real-time recognition and optimization of conversion elements in digital advertisements. The framework integrates enhanced optical character recognition with deep learning techniques to automatically identify, analyze, and optimize critical conversion elements including call-to-action buttons, pricing information, and value propositions. Experimental evaluation conducted on a dataset of 15,000 advertisements across multiple platforms demonstrates that DeepAd-OCR achieves 96.8% text recognition accuracy and 94.3% visual element detection accuracy, significantly outperforming traditional methods. Implementation across various industry sectors resulted in average conversion rate improvements of 22.8%, with e-commerce platforms experiencing the highest gains at 27.4%. The system's real-time optimization capabilities prove particularly valuable during time-sensitive promotions, dynamically adjusting emphasis elements based on performance metrics. Case studies validate the framework's effectiveness in practical applications, while acknowledging limitations in handling animated content and computational requirements. The DeepAd-OCR framework advances the state-of-the-art in advertisement optimization by combining sophisticated element recognition with adaptive optimization algorithms, enabling advertisers to maximize conversion potential through automated, data-driven adjustments.
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