| 摘要(英) |
In the process of human information communication, reading and writing are the most basic skills, and characters are the one of important parts. Therefore, in the automated handwriting recognition system, Chinese characters are more complicated and have a larger number of common words than English characters and numerals. Chinese characters possess the difference of font structure and stroke sequence. In-air handwriting scenario, each the Chinese characters has both features of real stroke and virtual stroke, and the presentation is different from handwriting on paper. Handwriting on paper only appears the real stroke, the virtual stroke couldn’t show on paper or screen because of lifting pen. However, when users handwrite in the air, the process of stroke is continuous and one stroke-finished; it makes the in-air handwriting own two characteristics: real and virtual strokes and time sequence.
Based on the above characteristics, this paper proposes to use the Long Short-Term Memory (LSTM) model as the core model for recognition. Deep learning requires a lot of training data. Although there are many institutions in China which devote to establish the Simplified Chinese dataset, it doesn’t fit the Taiwanese habit. Therefore, we collect 492 Traditional Chinese characters, about more than 20,000 data. To extract the turning point of the stroke through the preprocessing. In order to conform the characteristic of LSTM which fixed timing, the stroke is cut many fixed quantities, and been the input of recognition model by using shape context statistical spatial distribution feature. This paper test accuracy and stability by increasing and decreasing of strokes and setting the shape context of different dimensions. According experiments, the accuracy of recognizing Chinese characters is 98.6%.
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| 參考文獻 |
[1] Ning Xu, Weiqiang Wang, and Xiwen Qu, “Recognition of In-air Handwritten Chinese Character Based on Leap Motion Controller,” International Conference on Image and Graphics(ICIG), pp. 160-168, 2015.
[2] Xiaobo Mao, Zhiyuan Cheng, and Xiaodong Zhou, “Offline Handwritten Chinese Character Recognition Based on Concatenated Feature Maps,” J. Zhengzhou Univ.(Nat. Sei. Ed.), Vol. 50, No. 3, 2018.
[3] Haiqing Ren, Weiqiang Wang, Ke Lu, Jianshe Zhou, and Qiuchen Yuan, “An end-to-end recognizer for in-air handwritten Chinese characters based on a new recurrent neural networks,” 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 841-846, 2017.
[4] Zhuoyao Zhong, Lianwen Jin, and Zecheng Xie, “High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps,” 2015 13 th International Conference on Document Analysis and Recognition (ICDAR), pp. 846-850, 2015.
[5] Xiwen Qu, Weiqiang Wang, Ke Lu, and Jianshe Zhou, “In-air handwritten Chinese character recognition with locality-sensitive sparse representation toward optimized prototype classifier,” Pattern Recognition, Vol. 78, pp. 267-276, 2018.
[6] Xuyao Zhang, Fei Yin, Yanming Zhang, Chenglin Liu, and Yoshua Bengio, “Drawing and Recognizing Chinese Characters with Recurrent Neural Network,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, No. 4, pp. 849-862, 2017.
[7] 人機互動. [Accessed: 08-Apr-2019]. Available from: https://zh.wikipedia.org/wiki/人机交互.
[8] Serge Belongie, Jitendra Malik, and Jan Puzicha, “Shape matching and object recognition using shape contexts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 24, pp. 509-522, 2002.
[9] Tom ́aˇs Mikolov, Martin Karafi ́at, Luk ́aˇs Burget, Jan “Honza”ˇCernock ́y, and Sanjeev Khudanpur, “Recurrent neural network based language model,” Eleventh annual conference of the international speech communication association, 2010.
[10] Understanding LSTM Networks. [Accessed:15-Oct-2018]. Available from: http://colah.github.io/posts/2015-08-Understanding-LSTMs/.
[11] The Unreasonable Effectiveness of Recurrent Neural Networks. [Accessed:15-Oct-2018]. Available from: http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
[12] Sepp Hochreiter, and Jürgen Schmidhuber, “Long short-term memory,” Neural computation, Vol. 9, pp. 1735-1780, 1997.
[13] Vinod Nair, and Geoffrey E. Hinton, ”Rectified Linear Units Improve Restricted Boltzmann Machines,” Proceedings of the 27th International Conference on International Conference on Machine Learning, pp. 807-814, 2010.
[14] Deep Learning : the role of the activation function. [Accessed:18-Jan-2019]. Available from: https://mropengate.blogspot.com/2017/02/deep-learning-role-of-activation.html.
[15] Sergey Ioffe, and Christian Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” arXiv preprint arXiv:1502.03167, 2015.
[16] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” The Journal of Machine Learning Research, 2014.
[17] 鄒佩珊, “空中手寫中文字辨識,” 國立中央大學 資訊工程學系碩士論文, 2018.
[18] Xiwen Qu, Weiqiang Wang, Ke Lu, and Jianshe Zhou, “Data augmentation and directional feature maps extraction for in-air handwritten Chinese character recognition based on convolutional neural network,” Pattern Recognition Letters, Vol. 111, pp. 9-15, 2018.
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