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For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language, and collection. We propose a biologically inspired whole-word recognition method that is used to incrementally elicit word labels in a live Web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neurophysiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows us to classify text images that have a low frequency of occurrence. Typically, these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually, standard pattern-recognition technology cannot deal with these text images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.