Detection of Newspaper Layouts Using YOLO12

This study presents a robust and scalable method for automatic layout detection in digitized newspapers to facilitate efficient knowledge extraction and information retrieval. A custom dataset comprising annotated newspaper images in English, Hindi, and other languages was developed, with layout regions categorized into five primary classes. An enhanced YOLOv12 object detection model was trained on this dataset and evaluated using the mean Average Precision (mAP) metric across various Intersection over Union (IoU) thresholds. The model achieved a mAP@50 of 0.88, demonstrating strong detection performance and outperforming several stateof-the-art object detection models in the same task. The findings validate the effectiveness of the proposed approach in handling multilingual, structurally diverse newspaper formats. This research provides a practical framework for integrating automated layout analysis into digital archiving systems, OCR pipelines, and media monitoring applications. It also supports broader efforts to digitize historical print media and improve accessibility to regional content, thereby enabling enhanced research, journalism, and public engagement.