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Handwritten Word Image Matching Based on Heat Kernel Signature

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Computer Analysis of Images and Patterns (CAIP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8048))

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Abstract

Keyword Spotting is an alternative method for retrieving query words, without Optical Character Recognition (OCR), by calculating the similarity between features of word images rather than ASCII content. However, because of unconstrained writing styles with large variations, the retrieving results are always not very satisfactory.

In this paper, we propose a novel method, which is based on Heat Kernel Signature (HKS) and Triangular Mesh Structure to achieve handwritten word image matching. HKS can tolerate large variations in handwritten word images and capture local features. On the other hand, the triangular mesh structure is used to present global characteristics. Moreover, our method does not need pre-processing steps.

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Author information

Authors and Affiliations

  1. School of Computing, National University of Singapore, Singapore

    Xi Zhang & Chew Lim Tan

Authors
  1. Xi Zhang
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  2. Chew Lim Tan
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK

    Richard Wilson , Edwin Hancock , Adrian Bors  & William Smith , ,  & 

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Zhang, X., Tan, C.L. (2013). Handwritten Word Image Matching Based on Heat Kernel Signature. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-40246-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40245-6

  • Online ISBN: 978-3-642-40246-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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