TY  - CONF
AU  - Sounak Dey
AU  - Anguelos Nicolaou
AU  - Josep Llados
AU  - Umapada Pal
A2  - S+SSPR
PY  - 2016//
TI  - Local Binary Pattern for Word Spotting in Handwritten Historical Document
T2  - LNCS
BT  - Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)
SP  - 574
EP  - 583
KW  - Local binary patterns
KW  - Spatial sampling
KW  - Learning-free
KW  - Word spotting
KW  - Handwritten
KW  - Historical document analysis
KW  - Large-scale data
N2  - Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm.
L1  - http://refbase.cvc.uab.es/files/DNL2016.pdf
UR  - http://dx.doi.org/10.1007/978-3-319-49055-7_51
N1  - DAG; 600.097; 602.006; 603.053
ID  - Sounak Dey2016
ER  -