TY - GEN
T1 - Pre-processing of degraded printed documents by non-local means and total variation
AU - Likforman-Sulem, Laurence
AU - Darbon, Jérôme
AU - Barney Smith, Elisa H.
PY - 2009/1/1
Y1 - 2009/1/1
N2 - We compare in this study two image restoration ap-proaches for the pre-processing of printed documents: namely the Non-local Means filter and a total variation minimization approach. We apply these two approaches to printed document sets from various periods, and we evaluate their effectiveness through character recognition performance using an open source OCR. Our results show that for each document set, one or both pre-processing methods improve character recognition accuracy over recognition without preprocessing. Higher accuracies are obtained with Non-local Means when characters have a low level of degradation since they can be restored by similar neighboring parts of non-degraded characters. The Total Variation approach is more effective when characters are highly degraded and can only be restored through modeling instead of using neighboring data
AB - We compare in this study two image restoration ap-proaches for the pre-processing of printed documents: namely the Non-local Means filter and a total variation minimization approach. We apply these two approaches to printed document sets from various periods, and we evaluate their effectiveness through character recognition performance using an open source OCR. Our results show that for each document set, one or both pre-processing methods improve character recognition accuracy over recognition without preprocessing. Higher accuracies are obtained with Non-local Means when characters have a low level of degradation since they can be restored by similar neighboring parts of non-degraded characters. The Total Variation approach is more effective when characters are highly degraded and can only be restored through modeling instead of using neighboring data
U2 - 10.1109/ICDAR.2009.210
DO - 10.1109/ICDAR.2009.210
M3 - Conference contribution
AN - SCOPUS:71249113560
SN - 9780769537252
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 758
EP - 762
BT - ICDAR2009 - 10th International Conference on Document Analysis and Recognition
PB - IEEE Computer Society
T2 - 10th International Conference on Document Analysis and Recognition, ICDAR 2009
Y2 - 26 July 2009 through 29 July 2009
ER -