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Related Experiment Video

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A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
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Offline text-independent writer identification using a codebook with structural features.

Bashar Q Ahmed1, Yaser F Hassan2, Ashraf S Elsayed3

  • 1Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Alexandria, Egypt.

Plos One
|April 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel system for handwritten text-independent writer identification using contour-based features. The method achieves state-of-the-art performance on the IAM dataset, enhancing document authentication and forensic science.

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Area of Science:

  • Forensic Science
  • Authentication Science
  • Computer Vision

Background:

  • Handwritten document analysis is crucial for forensic and authentication science.
  • Text-independent writer identification remains a significant challenge.

Purpose of the Study:

  • To develop an offline system for text-independent writer identification of handwritten documents.
  • To introduce novel structural features for improved writer recognition.

Main Methods:

  • Extraction of handwritten connected component contours and segmentation.
  • Utilizing a bag-of-features approach with contour point curve angle and concavity/convexity features.
  • Training a k-means clustering algorithm to build a codebook and generating feature vectors via occurrence histograms.

Main Results:

  • The proposed system achieved state-of-the-art results on the English IAM dataset.
  • Competitive identification rates were obtained on the Arabic KHATT dataset.
  • The system demonstrated effectiveness using nearest neighbor and support vector machine classifiers.

Conclusions:

  • The proposed contour-based feature extraction method is effective for writer identification.
  • The system offers a robust solution for authenticating handwritten documents.
  • This approach advances the field of forensic document examination.