Application of Improved Deep Convolutional Neural Network Algorithm in Damaged Information Restoration

The repair of damaged documents has practical significance in multiple fields and can help people better analyze data information. This study proposes an improved algorithm model based on deep convolutional neural networks to address the issues of poor restoration performance and insufficient restoration information in the current process of restoring damaged document information. The new model improves the ability of document image classification and recognition data by using deep convolutional neural networks and incorporates grayscale rules to enhance the edge information restoration problem in the document information restoration process. The results indicated that in the repair of document data, the research model could achieve good document repair results. The average accuracy of the research model was 94.2%, which was 4.6% higher than the 89.6% of other models. The average percentage error of the model was around 3.6, which was about 2.2 lower than other models. The algorithm model used had the lowest average root mean square error of only 4.4, which was 1.9 lower than the highest model, and its stability was the best among several models. Therefore, the new model has a good repair effect in document information restoration, which has good guiding significance for the research of damaged information restoration.

@article{Jia2024,
  title     = {Application of Improved Deep Convolutional Neural Network Algorithm in Damaged Information Restoration},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Wenya Jia},
  doi       = {10.14569/IJACSA.2024.0150619},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150619}
}