Machine Printed Page Number Anomaly Detection Method Based on Multi-scale Self Attention Encoding Decoding

Paging is a particularly important step in the process of organizing archive files, and page numbers often appear missing, blurry, and other abnormal phenomena during the printing process. Effective detection of machine printed page numbers is of great practical significance for improving the efficiency of archive organization work; A machine printed page anomaly detection method based on multi-scale self attention encoding decoding is proposed to address the issue of poor performance of current general object detection methods and anomaly detection methods such as automatic encoders when migrating to the field of machine printed page anomaly detection; On the basis of the automatic encoder model, a deep network is adopted to replace the backbone network of the native automatic encoder encoding module, improving the model's feature extraction ability; A multi-layer network with self attention module acting on encoding and decoding modules was proposed to mine the information features of key areas of image targets, improve the image reconstruction ability of the decoding module, and enable the model to generate high-quality reconstructed images. By calculating the distance between the reconstructed image and the original image, anomaly detection of machine printed page numbers was achieved; Through experiments, it has been proven that the detection accuracy of this method reaches 98.7%, which better meets the needs of practical engineering.