Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set and a simple end-to-end pipeline demonstrate State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.

Sparse radial sampling LBP for writer identification / Nicolaou, Anguelos; Bagdanov, Andrew D.; Liwicki, Marcus; Karatzas, Dimosthenis. - ELETTRONICO. - 2015-:(2015), pp. 716-720. (Intervento presentato al convegno 13th International Conference on Document Analysis and Recognition, ICDAR 2015 tenutosi a Prouve Congress Center, fra nel 2015) [10.1109/ICDAR.2015.7333855].

Sparse radial sampling LBP for writer identification

BAGDANOV, ANDREW DAVID;
2015

Abstract

Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set and a simple end-to-end pipeline demonstrate State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.
2015
Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
13th International Conference on Document Analysis and Recognition, ICDAR 2015
Prouve Congress Center, fra
2015
Nicolaou, Anguelos; Bagdanov, Andrew D.; Liwicki, Marcus; Karatzas, Dimosthenis
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1081334
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