This technology is an advanced system that utilizes AI to automatically recognize, convert, and verify images of pharmaceutical documents (prescriptions, medication records, etc.) or computer screens captured via mobile devices in medical environments into highly accurate digital data.
1. Core Components
- AI Hybrid Processing Server: Extracts text (OCR) from captured images and categorizes it into meaningful data fields such as product names, quantities, and pricing.
- Pharmaceutical Master Data Server: Provides reference data to verify the integrity and consistency of the extracted information against existing official drug databases.
- Verification & User Terminals: Facilitates image capture and transmission while allowing human inspectors and users to finalize and calibrate any errors identified by the AI.
2. Key Technical Features
- Hybrid Recognition (Digital Screens vs. Printouts): The AI autonomously analyzes irregularities in the shooting environment—such as screen glare/moiré patterns from monitors or wrinkles on paper documents—to apply optimal pre-processing and recognition techniques.
- Integrity and Consistency Verification: Beyond simple character recognition, the system performs "accounting-level" checks on item codes, unit prices, quantities, and total sums to ensure the highest level of data reliability.
- Error-Based Self-Learning: The system systematically collects data where recognition errors occur to retrain the AI models, ensuring that system accuracy continuously evolves and improves over time.
3. Expected Benefits
- Operational Efficiency: Automates tasks previously handled through manual entry and inspection, drastically reducing processing time and preventing human error.
- High Precision: Achieves near 99.9% data consistency through an automatic calibration function integrated with master databases.
- Flexible Field Application: Enables immediate data collection through simple photography without the need for complex system integration, making it highly adaptable to various medical and distribution environments.
This technology serves as the critical foundation for converting the "Deep Context" of complex pharmaceutical sales statistics into precise, actionable digital data.