The effect of SIFT features as content descriptors in the context of automatic writer identification in Malayalam language

The span of writer identification extends to broad domes like digital rights administration, forensic expert decision-making systems, and document analysis systems and so on. As the success rate of a writer identification scheme is highly dependent on the features extracted from the documents, the phase of feature extraction and therefore selection is highly significant for writer identification schemes. In this paper, the writer identification in Malayalam language is sought for by utilizing feature extraction technique such as Scale Invariant Features Transform (SIFT). The schemes are tested on a test bed of 280 writers and performance evaluated.