Sheet Discrimination Using Server History and Image Recognition

The system performs preliminary registration of genuine sheets during a setup phase, storing their unique identification data (such as serial numbers, watermarks, or security features) in a database. When a sheet is inspected, the system compares its identification data against the pre-registered genuine sheets. This preliminary action enables the system to detect forged sheets by identifying mismatches, significantly improving detection reliability without requiring complex real-time analysis during the inspection phase.

The system incorporates feedback mechanisms where inspection results are continuously compared against stored reference data about sheet circulation history and condition. The system provides feedback loops that adjust classification criteria based on accumulated data about sheets of the same type, allowing it to identify patterns of degradation over time. This enables more accurate assessment of sheet condition without significantly increasing processing time, as the feedback is integrated into the existing inspection workflow.

The inspection process is segmented into multiple independent stages: initial rapid screening based on key identification features, followed by detailed inspection only for sheets that pass or fail the initial screen. The segmentation allows the system to process most sheets quickly through the simplified initial stage while maintaining high accuracy by applying comprehensive inspection only when necessary. This segmented approach significantly improves overall processing speed while preserving inspection quality for critical decisions.