Proposes a method for adaptively acquiring templates for degraded characters in scene images. Characters in scene images are often degraded because of poor printing and viewing conditions. To cope with the degradation problem, we proposed the idea of "context-based image templates" which include neighboring characters of parts thereof and so represent more contextual information than single-letter templates. However, our previous method manually selects the learning samples to make the context-based image templates and is time-consuming. Therefore, we attempt to make the context-based image templates automatically from single-letter templates and learning text-line images. The context-based image templates are iteratively created using the k-nearest neighbor rule. Experiments with 3,467 alpha-numeric characters in nine bookshelf images show that the high recognition rates for test samples possible with this method asymptotically approach those achieved with manual selection.
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