  Automatic unknown word detection techniques can enable new applications for
assisting English as a Second Language (ESL) learners, thus improving their
reading experiences. However, most modern unknown word detection methods
require dedicated eye-tracking devices with high precision that are not easily
accessible to end-users. In this work, we propose GazeReader, an unknown word
detection method only using a webcam. GazeReader tracks the learner's gaze and
then applies a transformer-based machine learning model that encodes the text
information to locate the unknown word. We applied knowledge enhancement
including term frequency, part of speech, and named entity recognition to
improve the performance. The user study indicates that the accuracy and
F1-score of our method were 98.09% and 75.73%, respectively. Lastly, we
explored the design scope for ESL reading and discussed the findings.
