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Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting.

Subhranil Kundu1, Samir Malakar2, Zong Woo Geem3

  • 1Department of Electronics and Communication Engineering, National Institute of Technology Durgapur, Durgapur 713209, India.

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Summary
This summary is machine-generated.

This study introduces a novel, learning-free method for handwritten keyword spotting (KWS) using a query-by-example approach. The proposed technique outperforms existing methods and even deep learning models on benchmark datasets.

Keywords:
Hough transformdynamic time warpinghandwritten wordkeyword spottingquery by example

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Area of Science:

  • Document Image Analysis
  • Pattern Recognition
  • Information Retrieval

Background:

  • Handwritten keyword spotting (KWS) is crucial for accessing historical and modern documents.
  • Existing methods often rely on machine learning, limiting their applicability in certain scenarios.

Purpose of the Study:

  • To develop an effective learning-free keyword spotting method for handwritten documents.
  • To establish a baseline for comparison with more complex, learning-based approaches.

Main Methods:

  • A four-step process: pre-processing, vertical zone division, Hough transform-based feature extraction, and feature matching.
  • Utilizes query-by-example (QBE) for spotting keywords.

Main Results:

  • The proposed learning-free KWS method demonstrates superior performance compared to state-of-the-art learning-free methods on IAM, QUWI, and ICDAR KWS 2015 datasets.
  • The custom features extracted using the Hough transform outperform deep features from InceptionV3, VGG19, and DenseNet121.

Conclusions:

  • The developed learning-free KWS method is a viable and effective alternative for handwritten document analysis.
  • The proposed feature extraction and matching strategy offers competitive performance without requiring extensive training data.