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  • 1.
    Das Chakladar, Debashis
    et al.
    Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India.
    Datta, Sumalyo
    Department of Electronics and Communication Engineering, Institute of Engineering Management, Kolkata, India.
    Roy, Partha Pratim
    Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India.
    Prasad, Vinod A.
    Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong.
    Cognitive Workload Estimation Using Variational Autoencoder and Attention-Based Deep Model2023In: IEEE Transactions on Cognitive and Developmental Systems, ISSN 2379-8920, Vol. 15, no 2, p. 581-590Article in journal (Refereed)
  • 2.
    Das Chakladar, Debashis
    et al.
    Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India.
    Roy, Partha Pratim
    Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India.
    Iwamura, Masakazu
    Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan.
    EEG-Based Cognitive State Classification and Analysis of Brain Dynamics Using Deep Ensemble Model and Graphical Brain Network2022In: IEEE Transactions on Cognitive and Developmental Systems, ISSN 2379-8920, Vol. 14, no 4, p. 1507-1519Article in journal (Refereed)
  • 3.
    Keserwani, Prateek
    et al.
    Indian Institute of Technology, Roorkee, India.
    Saini, Rajkumar
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Roy, Partha Pratim
    Indian Institute of Technology, Roorkee, India.
    Robust Scene Text Detection for Partially Annotated Training Data2022In: IEEE transactions on circuits and systems for video technology (Print), ISSN 1051-8215, E-ISSN 1558-2205, Vol. 32, no 12, p. 8635-8645Article in journal (Refereed)
    Abstract [en]

    This article analyzed the impact of training data containing un-annotated text instances, i.e., partial annotation in scene text detection, and proposed a text region refinement approach to address it. Scene text detection is a problem that has attracted the attention of the research community for decades. Impressive results have been obtained for fully supervised scene text detection with recent deep learning approaches. These approaches, however, need a vast amount of completely labeled datasets, and the creation of such datasets is a challenging and time-consuming task. Research literature lacks the analysis of the partial annotation of training data for scene text detection. We have found that the performance of the generic scene text detection method drops significantly due to the partial annotation of training data. We have proposed a text region refinement method that provides robustness against the partially annotated training data in scene text detection. The proposed method works as a two-tier scheme. Text-probable regions are obtained in the first tier by applying hybrid loss that generates pseudo-labels to refine text regions in the second-tier during training. Extensive experiments have been conducted on a dataset generated from ICDAR 2015 by dropping the annotations with various drop rates and on a publicly available SVT dataset. The proposed method exhibits a significant improvement over the baseline and existing approaches for the partially annotated training data.

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