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Efficient Caoshu Character Recognition Scheme and Service Using CNN-Based Recognition Model Optimization.

Boseon Hong1, Bongjae Kim2

  • 1Artificial Intelligence Research Center, Korea Electronics Technology Institute, Seongnam 13488, Korea.

Sensors (Basel, Switzerland)
|August 23, 2020
PubMed
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This study introduces an optimized Convolutional Neural Network (CNN) model for recognizing Caoshu characters, achieving high accuracy. The developed Caoshu character recognition system performs in real-time via an Android application.

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Machine Learning

Background:

  • Deep learning models, particularly Convolutional Neural Networks (CNNs), excel in image recognition tasks.
  • Caoshu, a cursive script, presents significant challenges for automated character recognition due to its complex and fluid nature.

Purpose of the Study:

  • To develop and optimize a CNN-based model for accurate Caoshu character recognition.
  • To enhance the model's performance using image pre-processing and data augmentation techniques.
  • To implement the optimized model as a real-time Android application for Caoshu recognition.

Main Methods:

  • Utilized Convolutional Neural Network (CNN) architecture for character recognition.
  • Applied image pre-processing and data augmentation to a Caoshu dataset.
Keywords:
Caoshu recognitionconvolutional neural networksdata augmentationmobile servicesmodel optimization

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  • Trained and validated the optimized CNN model.

    • Main Results:

    • Achieved a TOP-1 recognition accuracy of approximately 98.0% during model validation.
    • The optimized model demonstrated testing performance with accuracy (88.12%), precision (81.84%), recall (84.20%), and F1 score (83.0%).
    • Successfully developed and deployed a real-time Caoshu recognition service on an Android platform.

    Conclusions:

    • The optimized CNN model significantly improves Caoshu character recognition accuracy.
    • The developed Android application provides a practical and efficient solution for real-time Caoshu recognition.
    • The study validates the effectiveness of deep learning techniques in addressing complex character recognition challenges.