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Abstract:
We present a study which is concerned with word recognition
rates for heavily degraded documents. We compare human with machine
reading capabilities in a series of experiments, which explores the
interaction of word/non-word recognition, word frequency and
legality of non-words with degradation level. We also study the
influence of character segmentation, and compare human performance
with that of our artificial neural network model for reading. We
found that the proposed computer model uses word context as
efficiently as humans, but performs slightly worse on the pure
character recognition task.
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