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This paper introduces a deep learning-based framework for identifying hand-drawn schematics of power converter circuits and performing automated simulations. The framework employs cutting-edge computer vision-based object detection models, such as YOLOv8, to achieve a high mean average precision (mAP) of 96.7% to accurately identify components. Wire tracing and connectivity are achieved through a combined architecture built upon classical image processing techniques and deep learning approaches. Detailed information extracted from a hand-drawn circuit schematic is used to automatically create its netlist for automated simulation through the spice engine. The proposed framework is successfully tested on various nonisolated (buck, boost) and isolated (flyback, full-bridge) converters under both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) operations. In the comprehensive assessment of the entire framework, its efficacy is tested on 140 newly drawn circuit diagrams. The overall accuracy in the generation of netlists reaches a high value of 95.71%, utilizing the robust component detection capabilities of YOLOv8. Moreover, the framework enables the generation of both graphical representations and adjacency matrices for circuit diagrams. This output serves as a valuable dataset generator, contributing to the rapidly advancing domains of machine learning, including graph neural networks and geometric learning, particularly in the application space of power and energy systems. This framework can be further employed as an educational tool, and the ideas introduced can be developed to generate fully automated and efficient power converter designs for real-world applications.