In order to accurately recognize textual images of a book, we often employ various models including iconic
model (for character classification), dictionary (for word recognition), character segmentation model, etc.,
which are derived from prior knowledge. Imperfections in these models affect recognition performance inevitably.
In this paper, we propose an unsupervised learning technique that adapts multiple models on-the-fly
on a homogeneous input data set to achieve a better overall recognition accuracy fully automatically. The
major challenge for this unsupervised learning process is, how to make models improve rather than damage
one another? In our framework, models measure disagreements between their input data and output data.
We propose a policy based on disagreements to adapt multiple models simultaneously (or alternately) safely.
We will construct a book recognition system based on this framework, and demonstrate its feasibility.