| Summary: | This review paper finely analyzes eye-to-text communication and explores the development and evaluation of an advanced eye-controlled communication system. The study focuses on the algorithms for eye tracking and gaze estimation, the Artificial Neural Networks (ANNs), and the training process for converting eye movements into textual output. The article highlights Computer Vision (CV) algorithms utilized for real-time eye tracking and gaze estimation, emphasizing the robustness and accuracy of the algorithms. The authors detail the techniques employed, including pupil detection, iris segmentation, and gaze estimation, highlighting their effectiveness in capturing and analyzing eye movements. Moreover, the article discusses challenges faced during training that provide insights into potential improvements for future work. The review paper presents comprehensive experimental results, including ANN comparisons with existing methods such as gaze estimation error and user satisfaction, thoroughly assessing the system's capabilities. The system enables users to express themselves and interact with digital devices more independently, enhancing the Quality of Life (QoL) for individuals with limited motor abilities. This review aims to pinpoint the deficiencies left unaddressed by researchers, including issues such as head motion, low illumination, low-resolution cameras, and user fatigue. These identified shortcomings emphasize the pressing demand for eye-to-text communication systems. © 2024 Author(s).
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