Writer Identification in Music Score Documents without Staff-Line Removal

Abstract

Writer identification from musical scores is a challenging task. A few pieces of work on writer identification in musical sheets have been published in the literature but to the best of our knowledge all these work were performed after removal of staff lines from the musical scores. In this paper we propose a symbol-independent writer identification framework using HMM in music score without removing staff lines. The writing style of each writer is modeled using sliding window based LGH feature. To identify the writer of an input musical sheet, all musical lines are fed to writer specific HMM models and each model return a log-likelihood score for the given input. These log-likelihood scores from each HMM models are compared and the writer corresponding to the maximum score is considered as identified writer of the test sample. Next, a page level log-likelihood score is computed for writer identification in each page sample. We have compared our proposed approach with Gaussian Mixture Models (GMMs) based writer identification system in CVC-MUSCIMA data set. The results obtained from an experiment on 50 writers show that the HMM based approach outperforms GMM based approach.