Feasibility Study on Restoring Chinese Documents by Convolutional Autoencoder

Digitalizing documents has become a crucial aspect of modern information management. However, various factors, such as manual handwriting, stains, and degradation, can significantly impact the quality of digitalized documents. This paper proposes a autoencoder-based network cleaning technology to completely erase manual writing, stains, colors, and background noise from paper documents to enhance their digitalized quality. Our proposed approach utilizes machine learning algorithms to identify and isolate unwanted elements from the document and then replace them with clean and transparent backgrounds. The experimental results demonstrate that our proposed approach significantly improves the digitalized quality of paper documents, making them more suitable for long-term storage and retrieval. Our proposed technology has the potential to revolutionize the field of paper document digitalization and improve the efficiency of information management.