Context-Dependent Robust Text Recognition using Large-scale Restricted Bayesian Network

We have been proposing a computational model of the cerebral cortex called BESOM, which models the cerebral cortex as restricted Bayesian networks based on recent findings in the neuroscience area. Since BESOM is based on Bayesian network, it inherently allows bi-directional information flow, meaning that it can naturally merge information extracted from concrete data with highly-abstract prior knowledge. As an example of such kind of tasks, we report robust text recognition task with context information. We show that word spelling knowledge and word n-gram could be represented as a part of the network and they contribute the text recognition accuracy with noisy text images. We also show that the computational cost is approximately linear with the number of characters and words.