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Related Experiment Videos

Normalization-cooperated gradient feature extraction for handwritten character recognition.

Cheng-Lin Liu1

  • 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China. liucl@nlpr.ia.ac.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 15, 2007
PubMed
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A new normalization-cooperated gradient feature (NCGF) improves character recognition by reducing stroke distortion. This method enhances accuracy compared to traditional gradient features, especially for handwritten scripts.

Area of Science:

  • Computer Vision
  • Pattern Recognition
  • Machine Learning

Background:

  • Gradient direction histogram features are effective for character recognition.
  • Shape normalization can distort stroke direction, impacting recognition accuracy.
  • Existing methods struggle to mitigate normalization-induced distortions.

Purpose of the Study:

  • To introduce a novel feature extraction method, normalization-cooperated gradient feature (NCGF).
  • To improve character recognition accuracy by addressing stroke direction distortion.
  • To develop a feature extraction technique compatible with various normalization methods.

Main Methods:

  • Developed the normalization-cooperated gradient feature (NCGF) extraction approach.
  • Mapped gradient direction elements directly to direction planes without image normalization.

Related Experiment Videos

  • Combined NCGF with pseudo-two-dimensional normalization.

    • Main Results:

    • NCGF significantly reduces recognition error rates compared to normalization-based gradient features.
    • Error rate reductions ranged from 8.63% to 14.97% with high statistical significance.
    • The proposed method demonstrated effectiveness on handwritten Japanese and Chinese character databases.

    Conclusions:

    • NCGF is a robust feature extraction method for character recognition.
    • The approach effectively alleviates stroke direction distortion caused by normalization.
    • NCGF offers a significant improvement in recognition accuracy, particularly when combined with specific normalization techniques.