The Design Scheme of OCR and Analysis Platform for Power Meter Identification

This paper proposes a new design scheme for an OCR platform. The scheme first pre-trains the model on a standard dataset to improve its basic recognition ability, and then fine-tunes the pre-trained model using transfer learning techniques to a custom scenario, thus improving its recognition ability in power industry applications. This approach greatly reduces the need for annotated data and can quickly adapt to new meter types and updates. Additionally, an intelligent data collection, processing, and analysis platform can effectively help power companies monitor, measure, diagnose, and predict equipment and meters, thereby enhancing enterprise safety and stability. By introducing transfer learning-related techniques, the model’s knowledge learned on a standard dataset is transferred to a custom scenario, improving its recognition ability in power industry-specific applications and achieving rapid warning and diagnosis of abnormal situations in power equipment, further improving production efficiency and safety of the enterprise.