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Reading Progress Tracking Using Convolutional Neural Networks on High-Noise Eye-Tracking Data
| Author(s): | Shangareev A. I., Stupnikov S. A. |
| Published: | Pattern Recognition and Image Analysis, 2024. Vol. 34. Iss. 4. P. 1–10. |
| Abstract: | |
| The paper is devoted to studying the methods for tracking the reading progress on eye-tracking data using deep learning neural networks. An architecture of the autoencoder neural network is developed that is intended for efficient use the spatial and temporal information. A data augmentation method is proposed that generates high noise data and preserves information about the correspondence of each gaze fixation to a corresponding word. The quality of the neural network model is experimentally evaluated on noisy data.
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| Download: |
[ https://link.springer.com/article/10.1134/S1054661824050018 ]
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