Year 2019

• Evaluating Performance and Accuracy Improvements for Attention-OCR

  Publication

  • A. Brzeski
  • K. Grinholc
  • K. Nowodworski
  • A. Przybyłek

  - Year 2019

  In this paper we evaluated a set of potential improvements to the successful Attention-OCR architecture, designed to predict multiline text from unconstrained scenes in real-world images. We investigated the impact of several optimizations on model’s accuracy, including employing dynamic RNNs (Recurrent Neural Networks), scheduled sampling, BiLSTM (Bidirectional Long Short-Term Memory) and a modified attention model. BiLSTM was...

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• Residual MobileNets

  Publication

  • A. Brzeski
  • K. Grinholc
  • K. Nowodworski
  • A. Przybyłek

  - Year 2019

  As modern convolutional neural networks become increasingly deeper, they also become slower and require high computational resources beyond the capabilities of many mobile and embedded platforms. To address this challenge, much of the recent research has focused on reducing the model size and computational complexity. In this paper, we propose a novel residual depth-separable convolution block, which is an improvement of the basic...

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