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.
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