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Abstract. This paper presents a novel approach for off-line text-independent writer identification using Arabic handwritten images. Exploiting the idea that graphical fragments of handwriting characterize the writer, we propose a local approach based on texture analysis of small writing fragments where each fragment is represented by its Local Binary Pattern (LBP) histogram. The proposed method benefits from the efficiency of the LBP as a texture descriptor and the high discriminative power of handwritten fragments to improve the performance of writer identification. The proposed technique evaluated on a database of 130 writers realizes promising identification rates with reduced execution time.

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Writer Identification has gained increasing importance in the scientific community in recent years. In this paper, we propose an approach based on the combination of local textural descriptors and encoding methods (VLAD and Triangulation Embedding). The tests carried out in the bilingual LAMIS dataset made it possible to reach 100% in the Arabic version and 100% in the French version.

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