Writer identification using texture descriptors of handwritten fragments

Abstract

This paper presents a texture based approach for identification of writers from offline images of handwriting. Contrary to the classical texture based techniques which extract texture information at page or block level, we exploit the texture at a very small observation scale. The proposed technique divides a given handwriting into small fragments and considers each fragment as a texture. Texture descriptors including histograms of Local Binary Patterns (LBP), Local Ternary Patterns (LTP) and Local Phase Quantization (LPQ) are then computed from these fragments. The writer of a document is characterized by the set of histograms calculated from all the fragments in the writing. Two writings are compared by computing the distance between the descriptors of their writing fragments. The technique evaluated on IFN/ENIT and IAM databases comprising handwritten text in Arabic and English, respectively, realized high identification rates.