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Drawing and Recognizing Chinese Characters with Recurrent Neural Network.

Xu-Yao Zhang, Fei Yin, Yan-Ming Zhang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Area of Science:

    • Artificial Intelligence
    • Natural Language Processing
    • Computer Vision

    Background:

    • Deep learning has advanced handwriting recognition.
    • Chinese characters are a widely used writing system.
    • Existing methods primarily focus on recognition, not generation.

    Purpose of the Study:

    • To develop a unified recurrent neural network (RNN) framework for both Chinese character recognition and generation.
    • To propose an end-to-end system that directly processes sequential handwriting data.
    • To explore the dual role of RNNs as discriminative and generative models.

    Main Methods:

    • Utilized a recurrent neural network (RNN) system, incorporating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) components.
    • Developed a conditional generative model with character embedding for drawing Chinese characters.
    • Treated handwriting trajectory as sequential data, avoiding image-like transformations.

    Main Results:

    • Achieved state-of-the-art performance on the ICDAR-2013 handwriting recognition competition dataset.
    • Generated recognizable Chinese characters in vector format using the RNN framework.
    • Demonstrated high accuracy in recognizing generated characters with the discriminative RNN model.

    Conclusions:

    • Recurrent neural networks (RNNs) are effective as both discriminative and generative models for Chinese character tasks.
    • The proposed end-to-end RNN approach offers a domain-knowledge-free solution for handwriting recognition and generation.
    • The framework successfully bridges the gap between recognizing and automatically writing Chinese characters.