An Automatic Paleography Script Recognition System for the Arabic Language based on Fast Independent Component Analysis (Fast-ICA) and Support Vector Machine (SVM)

Paleography is the study of historical writing and its concern with identifying the date, origin, author(s), and other information about a particular script. There are many paleography text analysis system aims to analyze English handwritten text. However, scarcely any of them are proposed to analyze the Arabic handwritten texts. Hence, the Arabic paleographers are forced to manually analyzes the handwritten text. To facilitate and reduce the amount of time required to analyze the scripts for paleographers and archaeologists. An Automatic Paleography Script Recognition (APSR) System for the Arabic language is proposed in this study. The APSR has three main steps: preprocessing, feature extraction using Fast Independent Component Analysis (Fast-ICA), and recognition using Polynomial, Linear, and Gaussian Support Vector Machine (SVM) classifiers. In the proposed system, the images of the scripts are normalized into a uniform size at first. Afterward, the image noise is reduced using Gaussian blur. Subsequently, the script skeleton is extracted using the Erosion operator for feature extraction and selection of the handwritten script using Fast-ICA. The accuracy of the proposed system is evaluated on version 2 of 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 OHATRS, which is based on Principle Component Analysis (PCA) and SVM classifiers using several normalization sizes of Arabic text images. The experiential result shows the effectiveness of the proposed system compared to the OHATRS model.