Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDAR.2013.169
Title: Segmentation-free keyword spotting for handwritten documents based on heat kernel signature
Authors: Zhang, X.
Tan, C.L. 
Issue Date: 2013
Citation: Zhang, X., Tan, C.L. (2013). Segmentation-free keyword spotting for handwritten documents based on heat kernel signature. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR : 827-831. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDAR.2013.169
Abstract: We propose a new segmentation-free method for keyword spotting in handwritten documents based on Heat Kernel Signature (HKS). After key points are located by the key point detector for SIFT on the document pages and the query image, HKS descriptors are extracted from a local patch centered at each key point. In order to locate the positions where the query image appears in document pages, we present a searching method which tries to locate a local zone which contains enough matching key points corresponding to the query image. Our method does not need any pre-processing steps. © 2013 IEEE.
Source Title: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
URI: http://scholarbank.nus.edu.sg/handle/10635/78340
ISSN: 15205363
DOI: 10.1109/ICDAR.2013.169
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