By a News Reporter-Staff News Editor at Computers, Networks & Communications -- Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Beijing, People’s Republic of China, by VerticalNews journalists, research stated, “Recent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world.”
Funders for this research include Strategic Priority Research Program of the Chinese Academy of Sciences, National Natural Science Foundation of China.
The news reporters obtained a quote from the research from the Chinese Academy of Sciences, “Previous research has mainly focused on recognizing handwritten Chinese characters. However, recognition is only one aspect for understanding a language, another challenging and interesting task is to teach a machine to automatically write (pictographic) Chinese characters. In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters. To recognize Chinese characters, previous methods usually adopt the convolutional neural network (CNN) models which require transforming the online handwriting trajectory into image-like representations. Instead, our RNN based approach is an end-to-end system which directly deals with the sequential structure and does not require any domain-specific knowledge. With the RNN system (combining an LSTM and GRU), state-of-the-art performance can be achieved on the ICDAR-2013 competition database. Furthermore, under the RNN framework, a conditional generative model with character embedding is proposed for automatically drawing recognizable Chinese characters. The generated characters (in vector format) are human-readable and also can be recognized by the discriminative RNN model with high accuracy.”
According to the news reporters, the research concluded: “Experimental results verify the effectiveness of using RNNs as both generative and discriminative models for the tasks of drawing and recognizing Chinese characters.”
For more information on this research see: Drawing and Recognizing Chinese Characters with Recurrent Neural Network. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018;40(4):849-862. IEEE Transactions on Pattern Analysis and Machine Intelligence can be contacted at: Ieee Computer Soc, 10662 Los Vaqueros Circle, PO Box 3014, Los Alamitos, CA 90720-1314, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Pattern Analysis and Machine Intelligence - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34)
Our news correspondents report that additional information may be obtained by contacting X.Y. Zhang, Chinese Academy Sci, Inst Automat, NLPR, Beijing 100190, People’s Republic of China. Additional authors for this research include F. Yin, Y.M. Zhang, C.L. Liu and Y. Bengio.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TPAMI.2017.2695539. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-12), Chinese Academy of Sciences Reports Findings in Machine Learning (Drawing and Recognizing Chinese Characters with Recurrent Neural Network), Computers, Networks & Communications, 105, ISSN: 1944-1568, BUTTER® ID: 015460280
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