Writer Identification has great scope in emerging technology due to its usage in various types of applications in biometric and forensic science. Aim of this work is to identify the writer from script which is handwritten and obtained as scanned images. Features of textures will be elicited from wavelet decomposed images based on co-occurrence histograms. These will get the information about the relations among the sub bands of less frequency and that in sub bands of higher frequency at the particular level of the transformed image. If the co-relation between the sub bands has the same resolution then that indicates a stronger resolution. The relationship will indicate as information was essential considered to differentiating the textures. The proposed methodology will be executed with kannada handwritten document images by considering 14 different writers. Ability of features from texture in identifying writers is indicated through outcome achieved in experimentation.

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Wavelet Texture Feature Document Images Scanned Images Co-occurrence Histograms