TY - GEN
T1 - Combining Local Features for Offline Writer Identification
AU - Jain, Rajiv
AU - Doermann, David
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/9
Y1 - 2014/12/9
N2 - Several powerful approaches have recently been proposed for writer identification, which rely on local descriptors that capture the texture, shape and curvature properties of the handwriting. In this paper we use combinations of three of these features (K-Adjacent Segments, SURF, and Contour Gradient Descriptors), to address the writer identification problem. Experiments demonstrate that feature combinations outperform individual features, resulting in state-of-the-art performance on three datasets.
AB - Several powerful approaches have recently been proposed for writer identification, which rely on local descriptors that capture the texture, shape and curvature properties of the handwriting. In this paper we use combinations of three of these features (K-Adjacent Segments, SURF, and Contour Gradient Descriptors), to address the writer identification problem. Experiments demonstrate that feature combinations outperform individual features, resulting in state-of-the-art performance on three datasets.
KW - Feature Combination
KW - Handwriting
KW - Writer Identification
UR - https://www.scopus.com/pages/publications/84942239304
U2 - 10.1109/ICFHR.2014.103
DO - 10.1109/ICFHR.2014.103
M3 - Conference contribution
AN - SCOPUS:84942239304
T3 - Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
SP - 583
EP - 588
BT - Proceedings - 14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014
Y2 - 1 September 2014 through 4 September 2014
ER -