Revolutionizing of Sign Language Interpretation Using AI Modules

This paper presented a Robust License Plate Detection system combining OpenCV-based image processing with the k-Nearest Neighbors algorithm, specifically targeting the key limitations of traditional ANPR-OCR approaches. The proposed pipeline— encompassing Haar Cascade localization, morphological preprocessing, contour-based character segmentation, and CNN classification— achieves a test accuracy of 98.66% while operating at 30 FPS in real time on standard consumer hardware, without requiring an offline training phase. The system’s modular four-layer architecture— comprising Input, Processing, Intelligence, and Output/Persistence layers— ensures clear separation of concerns and facilitates future extensibility. The structured three-phase exper-imental validation, covering controlled indoor, variable outdoor, and stress-test conditions, confirms the system’s reliability and robustness across diverse real-world deployment environments. The SQLite-backed detection log-ging and Flask web interface provide a complete end-to-end solution suitable for immediate deployment in traffic monitoring checkpoints, smart parking facilities, and institutional access control gates.

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