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... More >

The healthcare sector has both opportunities and challenges as a result of the rapid expansion of unstructured clinical text data in electronic health records (EHRs). Physician notes, reports from radiologists, and summaries of discharge are examples of narrative medical documents from which relevant and actionable information can be extracted using clinical text analytics driven by Natural Language Processing (NLP). Named entity recognition, conceptual normalization, relation extraction, and temporal reasoning are just a few of the core methods and approaches in clinical natural language processing that are thoroughly covered in this paper. It covers cutting-edge deep learning models like B... More >