Research on Archival Image Recognition and Digital Reconstruction Methods Based on Artificial Intelligence

With the arrival of the information age, a large number of paper archives face the urgent need for digital transformation, and the traditional archive scanning and manual entry methods have obvious limitations in terms of efficiency and accuracy. In order to improve the level of archive digital processing, this paper proposes an artificial intelligence-based archive image recognition and digital reconstruction method, which aims to solve the key challenges of archive image quality degradation, complex structure, fuzzy text, etc., and to improve the extraction accuracy and reconstruction integrity of archive information. Firstly, a multi-stage image recognition architecture based on the combination of Convolutional Neural Network (CNN) and Transformer is constructed; secondly, CTPN text detection, CRNN character recognition and language model optimization strategies are fused to achieve highly robust recognition of complex archive content; lastly, Generative Adversarial Network (GAN) and super-resolution reconstruction model are introduced to repair and enhance the defective regions and low-quality text in the image, and improve the digitised image quality. Finally, Generative Adversarial Network (GAN) and super-resolution reconstruction model are introduced to repair and enhance the defective areas and low-quality text in the images, so as to improve the visual readability and information fidelity of the digitised archives.