Leveraging Transfer Learning for Article Segmentation in Historical Newspapers

Historical newspapers serve as invaluable resources for understanding past societies and preserving cultural heritage. However, digitizing these newspapers presents challenges due to their complex layouts and vast content. Article segmentation, involving the identification and extraction of individual articles from scanned newspaper images, is crucial for efficient information mining and retrieval. While some rule-based algorithms have been proposed, the applicability of deep neural networks (DNNs) for this task has recently gained attention. In this work, we explore the applicability of transfer learning to segment articles from historical newspaper images. For this, we employed nine pre-trained backbone architectures, specifically selected from the ResNet family, and proposed a bounding-box approximation based article segmentation module designed specifically for the task. Furthermore, we introduced a mean estimated article coverage metric that computes the segmentation capability of a model on an article-level. Experiments were conducted on the NAS dataset (NewsEye Article Separation), ensuring the relevance of our approach to historical data. Our study evaluates the performance of various pre-trained models, achieving a mean estimated article coverage of 0.956, 0.969, and 0.995 on the ONB, NLF, and BNF datasets, respectively. These findings underscore the effectiveness of transfer learning in adapting to historical layout analysis tasks, particularly article segmentation. Moreover, these results reaffirm the significance of transfer learning and pre-trained models as efficient tools for handling complex historical newspaper layouts.