Markus Schenkel, Cyril Latimer, Marwan Jabri
We present a study which is concerned with word recognition rates for heavily degraded documents. We compare human with machine read(cid:173) ing 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 segmen(cid:173) tation, 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.