A Generative Model for Multi-Dialect Representation

In the era of deep learning several unsupervised models have been developed to capture the key features in unlabeled handwritten data. Popular among them is the Restricted Boltzmann Machines (RBM). However, due to the novelty in handwritten multi-dialect data, the RBM may fail to generate an efficient representation. In this paper we propose a generative model – the Mode Synthesizing Machine (MSM) for on-line representation of real life handwritten multi-dialect language data. The MSM takes advantage of the hierarchical representation of the modes of a data distribution using a two-point error update to learn a sequence of representative multi-dialects in a generative way. Experiments were performed to evaluate the performance of the MSM over the RBM with the former attaining much lower error values than the latter on both independent and mixed data set.