Forensic Handwriting Identification System for the Arabic Language Based on Stationary Wavelet Transform (SWT) Fusion Technique

Forensic-handwriting text analysis aims to link two similar texts, an input text and a stored handwritten text, from a specific suspect using distinctive features, such as motion, hand pressure, and character shape. There are various forensic handwriting text analysis systems that aim to analyze English handwritten text. However, scarcely any of them are proposed to analyze the Arabic handwritten texts. Hence, the examiners or inspectors are forced to manually analyze the handwritten text. This can be tedious and time-consuming for inspectors. This study proposes an offline multistage forensic handwriting identification system for the Arabic language based on Stationary Wavelet Transform (SWT) Fusion Technique to facilitate and reduce the time required for the inspectors or forensic examiners to find similarities in handwritten texts. The proposed system has four main processes: Normalization and preprocessing feature extraction using Truncated Singular Value Decomposition (TSVD), Sparse Random Projection (SRP), and feature fusion using SWT, recognition using Polynomial, Linear and Gaussian SVM classifiers. The accuracy of the proposed system is evaluated using the IFN/ENIT dataset of handwritten Arabic text using Polynomial, Linear, and Gaussian SVM classifiers. Moreover, the accuracy result of the proposed system is compared with the accuracy result produced by a state-of-the-art HATRS, which is based on Local Binary Pattern and SVM classifiers using several normalization sizes of Arabic text images. The experiential result shows the effectiveness of the proposed system compared to the HATRS model. The best classification accuracy of the proposed system (98.83%) is obtained using the Gaussian SVM classifier.