Machine Learning Methods for Extracting Newspaper Articles from PDF Files

As digital archives of newspapers continue to grow, the need for automated methods to extract and organize information from PDF files becomes increasingly critical. This study addresses the challenge of layout extraction for newspaper articles using machine learning techniques. The objective is to develop a robust and scalable solution that accurately identifies and separates distinct elements within the complex layout of newspaper pages. The layout extraction of newspaper articles from PDF files is a crucial step in making the vast amount of historical and contemporary news content more accessible and searchable. Manual extraction is time-consuming and impractical for large datasets, necessitating the exploration of machine learning methods to automate this process. The primary objective of this research is to design and implement machine learning algorithms capable of accurately parsing the layout of newspaper articles within PDF files. The proposed methods aim to identify and delineate text, images, captions, headlines, and other elements present in the diverse layouts of newspaper pages. The study employs a combination of image processing and machine learning techniques. Initially, the PDF pages are converted into images, and preprocessing methods are applied to enhance the quality of the input data. Subsequently, a machine learning model, such as Convolutional Neural Networks (CNNs), is trained to recognize and classify different layout elements. Post-processing steps are implemented to refine the extraction results and improve the overall accuracy of layout segmentation.