Research on Recognition of Stamp Impression Types Based on Small Sample Quantity

To investigate the feasibility of classical computer vision models in recognizing stamp impression types with small sample quantity, as well as the impact of different stamping conditions.

Three network models, VGG16, ResNet50, and visual Transformer, were utilized to identify the kinds of stamp impressions imprinted by six common types of stamps, including photosensitive stamp, laser-engraved penetration stamp, self-inking stamp, copper stamp, wooden stamp, and rubber stamp.

In the experiment, the three network models performed well in recognizing all six types of stamp impressions. The recognition accuracy for photosensitive stamp, self-inking stamp, wooden stamp and rubber stamp all basically reached 100%, while only a slight decrease was observed in the recognition accuracy for laser-engraved penetration stamp and copper stamp. In the following blind test, the recognition accuracy of the three models for the six types of stamp impressions generally dropped by 3 to 35 percentage points, confirming the limitations of the network models in real complex scenarios.

Classical models in the field of computer vision can assist in recognizing the types of stamp impressions, but their recognition accuracy needs to be improved.