4.6 Article

An End-to-End Classifier Based on CNN for In-Air Handwritten-Chinese-Character Recognition

Journal

APPLIED SCIENCES-BASEL
Volume 12, Issue 14, Pages -

Publisher

MDPI
DOI: 10.3390/app12146862

Keywords

convolutional neural networks; in-air handwritten-Chinese-character recognition; end-to-end classifier; online handwritten-Chinese-character recognition; global average pooling

Funding

  1. National Natural Science Foundation of China Youth Fund [61906003]
  2. University Synergy Innovation Program of Anhui Province [GXXT-2021-004]

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In this paper, we propose an end-to-end classifier based on CNN for online handwritten-Chinese-character recognition. Our model directly takes the original coordinate sequence of a character as input and uses global average pooling to obtain a fixed-size feature vector for classification. Experimental results show that our method achieves higher recognition accuracy without the need for data augmentation or ensemble of multiple models.
A convolutional neural network (CNN) has been successfully applied to in-air handwritten-Chinese-character recognition (IAHCCR). However, the existing models based on CNN for IAHCCR need to convert the coordinate sequence of a character into images. This conversion process increases training and classifying time, and leads to the loss of information. In order to solve this problem, we propose an end-to-end classifier based on CNN for IAHCCR in this paper, which, to knowledge, is novel for online handwritten-Chinese-character recognition (OLHCCR). Specifically, our model based on CNN directly takes the original coordinate sequence of an in-air handwritten-Chinese-character as input, and the output of the full connection layer is pooled by global average pooling to form a fixed-size feature vector, which is sent to softmax for classification. Our model can not only directly process coordinate sequences such as the models based on recurrent neural network (RNN), but can also obtain the global structure information of characters. We conducted experiments on two datasets, IAHCC-UCAS2016 and SCUT-COUCH2009. The experimental results show a comparison with existing CNN models based on image processing or RNN-based methods; our method does not require data augmentation techniques nor an ensemble of multiple trained models, and only uses a more compact structure to obtain higher recognition accuracy.

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