Language identification in historical Afghan manuscripts

Automatic language identification is an important step prior to optical character recognition (OCR). In this paper we present a system to discriminate between Arabic and Persian in historical Afghan manuscripts. The classification is performed at a sub-sentence level. We propose a feature extraction algorithm for a sub-sentence based on Gabor filters followed by classification using a support vector machine (SVM). An overall precision of 96.72% and 94.90% is obtained for Persian and Arabic respectively.