Offline recognition of degraded numeral characters with MMTD-based fuzzy classifiers

DOI

10.1080/18756891.2014.853955How to use a DOI?

Keywords

the measure of medium truth degree (MMTD), classification based on MMTD (CBM), logistic regression, feature selection, offline recognition of degraded numerals

Abstract

Enabling machines to read like human beings has been a hot issue for more than fifty years. A novel offline degraded numeral recognition method (DNRBM) based on the measure of medium truth degree (MMTD) is proposed in this paper to identify segmented degraded numeral characters in gray images. It consists of distinguishing foreground from background, rotating an image, wiping off mottles, cutting margins, calculating both statistic and structural features, and recognizing numerals by the fuzzy classifiers constructed based on MMTD using features selected by logistic regression. The experimental results show that in comparison with the template matching method and the k-NN method, the proposed method performs well on recognizing degraded numeral characters with better scalability and better recognition performance.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Weiqing Cheng
AU  - Long Hong
AU  - Shaobai Zhang
PY  - 2014
DA  - 2014/01/01
TI  - Offline recognition of degraded numeral characters with MMTD-based fuzzy classifiers
JO  - International Journal of Computational Intelligence Systems
SP  - 113
EP  - 120
VL  - 7
IS  - Supplement 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.853955
DO  - 10.1080/18756891.2014.853955
ID  - Cheng2014
ER  -