Hi everyone,
Thank you to everyone that's replied thus far. I greatly appreciate all
the info! In hopes of making it easier for any incoming readers I have
updated the question site at:
http://www.walnutiq.com/#!questions-about-cla/cmr with any answers that
have already been given.
Best Regards,
Quinn Liu
[email protected]
On Mon, Jul 15, 2013 at 11:44 AM, Fergal Byrne
<[email protected]>wrote:
>
> 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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>
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