RECONSTRUCTION & ANALYSIS OF SHREDDED & RIPPED-UPDOCUMENTSUSING DEEP LEARNING FOR FORENSIC INVESTIGATION(ML)

Abstract

Reconstructing shredded and ripped-up documents is an essential component of forensic investigations, intelligence gathering, and legal evidence restoration. Criminals frequently destroy evidence by tearing or shredding documents to conceal information related to fraud, financial crimes, identity theft, and confidential operations. Traditional reconstruction methods rely heavily on manual labor, expert judgment, and time-consuming physical assembly. These manual processes are limited in scalability and accuracy, especially when handling thousands of irregular fragments generated by cross-cut shredders or irregular tearing patterns. With advancements in artificial intelligence, deep learning has emerged as a promising solution for automating the reconstruction of shredded documents. This paper presents a deep learning–driven framework that integrates computer vision, convolutional neural networks (CNNs), feature extraction, edge detection, similarity learning, and transformer-based OCR to reconstruct shredded and ripped documents with high accuracy. The proposed methodology begins with preprocessing and segmentation of shredded fragments, followed by CNNbased feature extraction to capture edge patterns, texture consistency, and shape signatures. A Siamese network architecture is employed to evaluate the similarity between fragment pairs and determine potential adjacency relationships. The reconstruction module utilizes graph-based alignment algorithms that combine edge compatibility scores with spatial arrangement predictions to generate a candidate layout for the reassembled document. Once reconstruction is complete or partially complete, an OCR-based text extraction module retrieves textual content from the reassembled page to support forensic interpretation. Experimental results demonstrate the model’s capability to reconstruct mechanically shredded, hand-torn, and irregularly fragmented documents under varying degrees of damage. Performance metrics indicate significant improvements in accuracy, time efficiency, and completeness compared to traditional methods. This research provides a scalable, intelligent, and automated approach for forensic teams, reducing reliance on manual sorting and improving investigation efficiency. The deep learning pipeline has the potential to assist law enforcement agencies, digital forensics experts, and intelligence organizations in cases where recovering destroyed documents is critical for solving crimes or preventing security threats. Overall, the proposed framework advances the application of AI in forensic science and establishes a foundation for future enhancements using generative models and multimodal learning.