Automated Classification of Noise in Historical Document Images

Abstract

The digitalization of historical documents is crucial for preserving valuable records for future generations. However, obtaining high-quality digital versions of these documents is challenging due to their often degraded state, characterized by low contrast and corrupted artifacts resulting from aging, environmental factors, and handling. These degradations make it difficult to read and utilize historical documents effectively. Recent research has focused on restoring and improving the quality of these degraded documents to ensure they remain accessible and usable. Accurate classification of noise types in documents is essential for applying the correct restoration methods to change the documents to a noise free document. Different types of noise require specific preprocessing techniques for effective removal, making precise noise identification a critical step in the restoration process. Our work aims to build a robust automated classification model that accurately identifies the type of noise in document images. By leveraging the learning and generalization capabilities of Convolutional Neural Networks (CNNs), our model classifies five main noise types ‗clean image, paper damage, transparency, faded image, spots‘, accurate classification enables the selection of appropriate noise removal method. This classification significantly improves the efficiency and effectiveness of document restoration processes. The promising results from our experiments highlight the potential of deep learning in enhancing the quality of digitized historical documents and ensuring their long-term accessibility.