 |
|
Related Communities:
|
 |
|
 |
Tracking Reading Progress Using an Auto-Encoding Neural Network
| Author(s): | Shangareev A. I., Shanin I. A. |
| Published: | Pattern Recognition and Image Analysis, 2024. Vol. 34. Iss. 3. P. 863–869. |
| Abstract: | |
| In the field of oculography tracking reading progress is challenging due to measurement errors in eye tracking systems. This paper presents a two-stage approach using an autoencoding neural network model. The model uses fixation duration and text structure to create a probability map representing the likelihood that each pixel is viewed and interprets this map using a specific criterion to determine whether a word has been read. The model was trained and evaluated using a synthetic dataset generated from the ZuCo 1.0 dataset. The model achieved an F-measure value of 0.9782 and an MIoU value of 0.9587 on a test set of 537 pages for the task of classifying words into read and unread.
|
| Download: |
[ https://link.springer.com/article/10.1134/S1054661824700755 ]
|
|
|
