Hi Michael, Handwritten characters are undoubtedly multi-component designs, which have evolved to connect with and trigger our ability to learn spatial, temporal and hierarchical patterns. We perceive the same characters even when loads of things change in fonts, and especially when reading different people's handwriting. We can fill in gaps and correct misspellings. So the learning and prediction must be several levels deep in hierarchy.
In terms of bottom level mechanics, we use saccades to recognise and "delocalise" components such as characters, facial features, etc, in such a way as to allow this multi-level recognition (including a hierarchy of fixations - for strokes, junctions, topology, characters, letters, words, and even sentences). Speed-readers can saccade to read entire phrases and sentences at a time, allowing reading speeds of thousands of words per minute with better than 70% comprehension scores. With practice, I've been able to get scores in the 1-2000 wpm range. I can also read text in a mirror or upside-down at speeds approaching 50-60% of an average reader. These things could only be done using big, complex region hierarchies with vast volumes of (normal) reading practice. I would have predicted that a single layer CLA would struggle with this kind of data set, because it lacks the multi-level upward and downward structure which I feel this kind of performance requires. Regards, Fergal Byrne
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