TY  - CONF
AU  - Ekta Vats
AU  - Anders Hast
AU  - Alicia Fornes
A2  - ICDAR
PY  - 2019//
TI  - Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion
BT  - 15th International Conference on Document Analysis and Recognition
SP  - 1294
EP  - 1299
KW  - Word spotting
KW  - Segmentation-free
KW  - Trainingfree
KW  - Query expansion
KW  - Feature matching
N2  - Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectorsand Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.
UR  - https://ieeexplore.ieee.org/document/8978077
L1  - http://refbase.cvc.uab.es/files/VHF2019.pdf
UR  - http://dx.doi.org/10.1109/ICDAR.2019.00209
N1  - DAG; 600.140; 600.121
ID  - Ekta Vats2019
ER  -