Structural learning for writer identification in offline handwriting

Availability of sufficient labeled data is key to the performance of any learning algorithm. However, in document analysis obtaining the large amount of labeled data is difficult. Scarcity of labeled samples is often a main bottleneck in the performance of algorithms for document analysis. However, unlabeled data samples are present in abundance. We propose a semi supervised framework for writer identification for offline handwritten documents that leverages the information hidden in the unlabeled samples. The task of writer identification is a complex one and our framework tries to model the nuances of handwriting with the use of structural learning. This framework models the complexity of learning problem by selecting the best hypotheses space by breaking the main task into several sub tasks. All the hypotheses spaces pertaining to the sub tasks will be used for the best model selection by retrieving a common optimal sub structure that has high correspondence with all of the candidate hypotheses spaces. We have used publically available IAM data set to show the efficacy of our method.