Research on intelligent detection technology of confidential documents based on OCR and semantic analysis

With the acceleration of digital transformation in the power industry, confidential document management faces the dual challenges of the failure of the traditional “human defense” mode and the lack of technical control means. This study proposes an intelligent detection and transmission control system for confidential documents based on OCR(Optical Character Recognition) and semantic analysis, and builds a complete technical system for dynamic identification of multimodal confidential identification, parsing of heterogeneous documents and real-time interception. First, the feature extraction capabilities of text, image and mixed documents are integrated through a multimodal fusion framework: the enhanced OCR engine based on CNN-LSTM realizes text parsing of unstructured images, combines regular expression matching and deep learning semantic analysis to build a dynamic weight dictionary library, and uses user feedback-driven online learning mechanism to improve the scene adaptability of identification recognition. Secondly, a unified document parsing engine is designed, and modular adapters are used to support structured parsing and streaming processing of multiple formats such as PDF, DOCX, and OFD. The cross-modal sensitive content recognition is realized through a rule-model collaborative detection framework, and the sub-second response speed is achieved in government and industrial data set tests. Finally, a transmission control system based on kernel-state monitoring and dynamic strategies was developed. Relying on device fingerprint identification, content sensitivity assessment and blockchain audit technology, millisecond-level illegal transmission detection, high-precision interception and verifiable auditing were achieved, building an integrated data security protection system of “technical defense + management” for the power industry.