Forensic Text Recovery from Document Thumbnails Using Text-Aware U-Net

Abstract

Forensic analysis of low-resolution Word thumbnails presents a significant challenge because these artifacts may constitute the only remaining visual evidence of deleted documents. Standard Optical Character Recognition (OCR) tools perform poorly on such severely degraded images, creating a critical bottleneck in forensic evidence recovery. Although deep learning architectures, such as U-Net, can improve visual image quality, models optimized solely using pixel-based loss functions tend to favor visual smoothness over the structural fidelity required for accurate text recognition. To address this limitation, we propose a text-aware restoration framework designed for forensic triage. The proposed approach employs a U-Net architecture guided by a hybrid optimization strategy that integrates pixel-level fidelity with machine-readable text constraints. Specifically, an OCR-based Normalized Levenshtein Distance (NLD) metric is used during model selection to prioritize the recovery of an accurate character topology. Experimental results demonstrate that the proposed method significantly reduces the NLD, enabling reliable recovery of machine-readable text from severely degraded thumbnails.