Script Recognition – 03 Human reading process prof. dr. L. Schomaker KI RuG.
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Transcript of Script Recognition – 03 Human reading process prof. dr. L. Schomaker KI RuG.
![Page 1: Script Recognition – 03 Human reading process prof. dr. L. Schomaker KI RuG.](https://reader030.fdocuments.us/reader030/viewer/2022032800/56649d4e5503460f94a2d714/html5/thumbnails/1.jpg)
Script Recognition – 03Human reading process
prof. dr. L. Schomaker
KI
RuG
![Page 2: Script Recognition – 03 Human reading process prof. dr. L. Schomaker KI RuG.](https://reader030.fdocuments.us/reader030/viewer/2022032800/56649d4e5503460f94a2d714/html5/thumbnails/2.jpg)
© Schomaker 2001
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Human Reading
• Eyes are no flat-bed scanners!
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© Schomaker 2001
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Human Reading
• Eyes are not flat-bed scanners!
– Fast movements: saccades
– Alternated with fixations: the high-resolution center of the retina is positioned to capture a limited area of the complete visual array
![Page 4: Script Recognition – 03 Human reading process prof. dr. L. Schomaker KI RuG.](https://reader030.fdocuments.us/reader030/viewer/2022032800/56649d4e5503460f94a2d714/html5/thumbnails/4.jpg)
© Schomaker 2001
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Human Reading
• Eyes are not statically positioned cameras
– Looking and reading involve an active process of selective attention, opportunistically jumping to the next area of visual interest
– The illusion of a global and consistent view of the world is generated by the integrative processes in the visual cortex (not in the eyes):
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© Schomaker 2001
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Human reading research (HRR)
• HRR is mostly guided by epistemologic dynamics intrinsic to the used experimental paradigms, less by fundamental questions concerning geometry and features of scripts influencing reading performance
• Findings are of little use to developers of reading systems
• Modeling efforts are limited, where models do exist, they concern toy problems (small dictionaries, oversimplified character shapes)
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© Schomaker 2001
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Global word shape
General framework: Merton’s “Logogen”:there exists a lexicon of words. Words get ascore of likelihood on the basis of detectedfeatures. This example: word contour pattern.
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© Schomaker 2001
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Tests with ascender/descender coding
a = x h = lx o = x v = x
b = lx I = x p = jx w = x
c = x j = j q = xj x = x
d = xl k = lx r = x y = xj
e = x l = l s = x z = x
f = l m = x t = l
g = xj n = x u = x
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© Schomaker 2001
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Code-length frequencies in 48k lexicon
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Code ‘collisions’ : amount of different words per code
Worst case: 1345 words for code ‘x’2^16 2^10 alternatives
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© Schomaker 2001
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However: humans do not seem to use word contour extensively, in the reading of machine-print words
“case-mixing” experiments
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Experiment on reading handwriting
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Reaction times, common words
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© Schomaker 2001
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Error rates, common words
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Reaction time, rare words
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© Schomaker 2001
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Error rates, rare words