Textual Feature Extraction and Machine Learning Classification for Document Forgery Detection

The authentication of scanned administrative documents is a critical challenge in digital forensics due to the accessibility of sophisticated image editing tools. While existing forgery detection methods focus on geometric patterns or hardware signatures, they often struggle with the structural complexity of Arabic cursive script or require original reference templates. This paper proposes a novel framework based on statistical intensity continuity analysis for detecting forgeries in scanned Arabic documents. The method targets the physical distinction between the stochastic noise introduced by analog scanning and the artificial uniformity of digital manipulation. We introduce a novel maximum continuous intensity value (MCIV) feature and utilize interquartile range (IQR) analysis to identify statistical outliers in text block intensity distributions. The proposed pipeline was evaluated on a newly created dataset of 609 Arabic administrative scans acquired through a multiscanner strategy. Experimental results using 10‐fold cross‐validation demonstrate that the random forest classifier achieves a peak accuracy of 84.70% and a forged‐class F1‐score of 89%. Comparative benchmarking against established methods from the literature shows that the proposed framework achieves a superior precision of 89.31% and significantly reduces the false positive rate (FPR) to 24.82%, providing a more reliable and defensible balance for forensic applications. Furthermore, the system demonstrates high computational efficiency (0.197 s per document), making it suitable for high‐volume forensic triage in real‐world administrative environments.