Conference Proceedings - 2021

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http://repository.vlu.edu.vn:443/handle/123456789/1422

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Browsing Conference Proceedings - 2021 by Subject ""Artificial neural networks"
  • Publication
    Efficient binarizing split learning based deep models for mobile applications
    ( 2021)
    Ngoc Duy Pham
    ;
    Hong Dien Nguyen
    ;
    Dinh Hoa Dang
    Split Neural Network is a state-of-the-art distributed machine learning technique to enable on-device deep learning applications without accessing to local data. Recently, Abuadbba et al. carried out the use of split learning to perform privacy-preserving training for 1D CNN models on ECG medical data. However, the proposed method is limited by the processing ability of resource-constrained devices such as mobile devices. In this paper, we attempt to binarize localized neural networks to reduce computation costs and memory usage that is friendly with hardware. Theoretically analysis and evaluation results show that our method exceeds BNN and almost reaches CNN performance, while significantly reducing memory usage and computation costs on devices. Therefore, on the basis of these results, we have come to the conclusion that binarization is a potential technique for implementing deep learning models on mobile devices
  • Publication
    OCR error correction for Vietnamese handwritten text using neural machine translation
    ( 2021)
    D. Q. Nguyen
    ;
    A. D. Le
    ;
    M. N. Phan
    ;
    P. Kromer
    ;
    I. Zelinka
    OCR post-processing is an important step for improving the quality of OCR output texts. Long short-term memory (LSTM) is a deep learning model, which has wide-range applications in many domains like time series prediction, natural language processing and speech recognition. In this paper, we propose an OCR error correction model using neural machine translation with bidirectional LSTM networks at syllable level. Vietnamese OCR text dataset for the model evaluation is outputted from an OCR engine based on the attention-based encoder-decoder (AED) model taking input of handwritten text in the benchmark database of the ICFHR 2018 Vietnamese online handwritten text recognition competition. The experimental results show that the proposed model helps decrease the word error rate in the OCR output texts of the above AED model by about 2%. The model performance is also discussed and compared to the other baseline methods in the competition