Writer identification in a handwritten document image using texture features

Recently, writer identification has been studied and it has a wide variety of applications, more specifically in biometric and forensic science. This paper presents the use of texture features in identifying the writer from the handwritten document image. The texture features are extracted based on the co-occurrence histograms of wavelet decomposed images, which capture the information about relationships between each high frequency subband and that in low frequency subband of the transformed image at the corresponding level. The correlation between the subbands at the same resolution exhibits a strong relationship, indicating that this information is significant for characterizing a texture. This scheme is tested on two scripts namely, Kannada and English. The experiments are performed by considering 5, 10, 15, 20, 25 and 30 writers at a time. The experimental results demonstrate the efficacy of the texture features in identifying the writer.