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A Pix2Pix Architecture for Complete Offline Handwritten Text Normalization.

Alvaro Barreiro-Garrido1, Victoria Ruiz-Parrado1, A Belen Moreno1

  • 1Higher Technical School of Computer Engineering, Universidad Rey Juan Carlos, c/Tulipan sn, Mostoles, 28922 Madrid, Spain.

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Summary
This summary is machine-generated.

This study introduces a Pix2Pix model for normalizing handwritten text images, improving offline handwritten text recognition. This trainable approach integrates seamlessly with deep learning models, matching or exceeding heuristic methods.

Keywords:
GANsIAM datasetdeep learningimage normalizationoffline handwritingpix2pixscanned text preprocessing

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Offline handwritten text recognition relies on preprocessing normalization algorithms.
  • Existing methods often use heuristic strategies not integrated with recognition models.
  • This limits the unified training of normalization and recognition components.

Purpose of the Study:

  • To introduce a Pix2Pix trainable model for normalizing handwritten text images.
  • To enable seamless integration of normalization as the initial stage in deep learning recognition architectures.
  • To facilitate unified training of normalization and recognition while maintaining module interpretability.

Main Methods:

  • Utilized a Pix2Pix conditional generative adversarial network for image normalization.
  • Trained the model on a blend of heuristic transformations to address handwriting variability.
  • Integrated the normalization approach as the first step in a deep recognition architecture.

Main Results:

  • Achieved slope and slant normalization, and normalized ascender/descender sizes.
  • The proposed method replicated and, in some cases, surpassed a widely used heuristic algorithm.
  • Demonstrated effectiveness across two metrics when integrated into a deep recognition architecture.

Conclusions:

  • The Pix2Pix model offers an effective, integrated approach to handwritten text normalization.
  • This method mitigates intra-personal handwriting variability, enhancing recognition performance.
  • The trainable normalization is a viable alternative to traditional heuristic preprocessing techniques.