Comparison of Human and Machine Word Recognition
Abstract
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.
Cite
Text
Schenkel et al. "Comparison of Human and Machine Word Recognition." Neural Information Processing Systems, 1997.Markdown
[Schenkel et al. "Comparison of Human and Machine Word Recognition." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/schenkel1997neurips-comparison/)BibTeX
@inproceedings{schenkel1997neurips-comparison,
title = {{Comparison of Human and Machine Word Recognition}},
author = {Schenkel, Markus and Latimer, Cyril and Jabri, Marwan A.},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {94-100},
url = {https://mlanthology.org/neurips/1997/schenkel1997neurips-comparison/}
}