24 January 2011 Multiple-agent adaptation in whole-book recognition
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Abstract
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.
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Pingping Xiu, Henry S. Baird, "Multiple-agent adaptation in whole-book recognition", Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740P (24 January 2011); doi: 10.1117/12.876751; https://doi.org/10.1117/12.876751
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