## 5 Conclusions  
The quality of text readability using telepresence robots is influenced by two main factors: camera hardware properties and image processing algorithms. Ohmni, Double 3, Temi 2, and Temi 3 TPRs are four different TPR models that were evaluated for text readability. It was found that Double 3 had the best overall readability, followed by Temi 3 and then the others. This was due to Double 3's dual-lens camera system, which provided better image quality and zoom capabilities. Ohmni's readability was hampered by its aggressive sharpening algorithms, which distorted the image and made the letters harder to distinguish. All TPR's image readability results were also affected by the exposure metering modes, which resulted in overexposed images in certain lighting conditions. It was also found that using optical zoom, rather than digital zoom, resulted in better image quality and text readability. Additionally, taking still images, rather than using video frames, also improved text readability. This is because still images can be captured at a higher resolution than video frames. In general, it is preferable to keep the room lighted when using TPRs for presentations. This will help to ensure that the text is readable and that the overall presentation quality is high. The examination of TPR camera capabilities for remote teaching and learning suggests avenues for future research. While the study compared the camera quality of different TPR models, exploring additional factors influencing OCR accuracy in these environments is valuable. Variables like lighting conditions and text complexity could impact OCR performance. Moreover, considering the crucial role of compression algorithms, or codecs, in shaping image quality, it becomes imperative to integrate this aspect into further research. Compression algorithms significantly affect video stream quality, and their dynamic adjustments during data transmission over the Internet can impact image quality. In the experiment, all three TPRs employed dynamic compression algorithms, but manufacturers provided limited technical details and restricted user settings, potentially leading to degraded video quality in low-bandwidth conditions. To minimize the impact of this factor, the study used a network with a bandwidth much larger than the required characteristics stated by the robot manufacturers. In addition, the study primarily focused on OCR accuracy for optometry charts and text on a projector screen. Future research could explore OCR performance in other applications, such as the real-time transcription of lectures or capturing whiteboard notes. Comparing TPR OCR accuracy with human vision under the same conditions could reveal the extent to which OCR can effectively replace human vision in remote teaching and learning scenarios. Furthermore, beyond technical aspects, the study emphasizes the importance of considering user experience and acceptance. Research could explore factors such as user comfort, perceived effectiveness, and potential barriers to the adoption of TPR-based remote learning. While the study involved controlled experiments, future research might implement TPRs in real-world educational settings to assess their effectiveness in enhancing teaching and learning outcomes. Additionally, subsequent research may further explore imaging principles, robot hardware, and related aspects, contributing to a comprehensive understanding of the impact of video codecs on TPR performance. Conceptualization, A.T., J.L. and S.V. ; methodology, A.T. and J.L. ; software, A.T.; validation, A.T., J.L. and S.V. ; formal analysis, S.V. ; investigation, A.T.; resources, A.T.; data curation, A.T.; writing--original draft preparation, A.T.; writing--review and editing, A.T., J.L. and S.V. ; visualization, A.T.; supervision, S.V. and J.L. ; project administration, A.T. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding.