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Specifically, the two-stage training strategy adopts pseudo-labeled samples firstly to train a deep neural network, then the deep neural network is refined using labeled samples one more time. As a result, more samples can be used for training a deep neural network, which is significant to the performance improvement of a deep neural network in the case of inadequate labeled samples. More importantly, the deep neural networks trained under the proposed learning framework perform better than the famous deep neural networks in a robot-written character identification experiment.<\/jats:p>","DOI":"10.3233\/jifs-221389","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T11:26:13Z","timestamp":1662117973000},"page":"7833-7846","source":"Crossref","is-referenced-by-count":1,"title":["A new end-to-end semi-supervised deep learning framework for mastering robot-written character identification"],"prefix":"10.1177","volume":"43","author":[{"given":"Qilong","family":"Jia","sequence":"first","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian, China"}]},{"given":"Song","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, University of Science and Technology Liaoning, AnShan, 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