## 4 Discussion  
The quality of the final result was determined by two main parameters: \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Double**} & \multicolumn{2}{c}{**Ohmni**} & \multicolumn{2}{c}{**Temi 3**} & \multicolumn{2}{c}{**Temi 2**} \\  & **Snellen** & **LogMAR** & **Snellen** & **LogMAR** & **Snellen** & **LogMAR** & **Snellen** & **LogMAR** \\ \hline Screenshot & 20/50 & 0.4 & 20/70 & 0.5 & - & - & - & - \\ Maximum zoom & 20/20 & 0.1 & 20/120 & 0.6 & 20/50 & 0.6 & 20/70 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the optometric measurements of LogMAR (lower values are better) and Snellen charts. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Robot Model**} \\
**Condition** & **Double 3** & **Ohmni** & **Temi 3** & **Temi 2** \\ \hline
5 m, lights on & 20 & 28 & 20 & 24 \\
5 m, lights off & 60 & x & 36 & 28 \\
5 m, screenshot & 12 & 54 & x & none \\
10 m, lights on, max zoom & 12 & 14 & 44 & 24 \\
10 m, lights on & 48 & 60 & 54 & 40 \\
10 m, lights off & X & X & X & 40 \\
10 m, lights on, max zoom & 44 & 60 & 44 & 40 \\
10 m, screenshot & 54 & 48 & none & none \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Projector image (lower values are better, X means no text was readable). Figure 7: Sample images of the slides in the dark room. * Camera hardware properties and quality,
* image processing algorithms implemented in software. Image processing is a general term for different types of manipulation of digital images, including noise reduction, adjusting contrast, brightness, or sharpness, and applying numerous types of filters. For example, Ohmni uses aggressive sharpening algorithms that distort the image and make the letters harder to distinguish. Although such image processing may add some benefits in other scenes, it clearly works as a disadvantage when reading text. Images, with a wide range of light intensities from the darkest to the brightest (wide dynamic range) are still a challenge to camera sensors; therefore, the text on the images obtained from the bright screen in the dark room is sometimes unreadable not due to a low camera resolution but to brightness adjustments made by TPR image processing algorithms. There are different exposure metering modes, such as center-weighted, matrix, or spot metering, with the first ones usually showing better results, but in such a particular case with a bright projector image in the dark environment, they result overexposure of the slide. Double 3 mitigates this problem partially using its zoom camera. In that case, the slide takes up a larger part of the image resulting in better balance. Ohmni has manual controls for contrast, sharpness, and exposure, but their usage is hardly feasible as lighting conditions may change, settings are unintuitive, and nobody in the experimental group was able to achieve significant improvement of the image quality by adjusting the settings manually. Although the readability of the slides in general will depend not only on the size of the letters, but on the background color, contrast, and technical specifications of the projector, this experiment provides comparative results. All TPRs demonstrate much worse readability results in situations where ambient light is turned off; therefore, it is preferable to keep the room lighted when showing slides. Ohmni demonstrated a significantly better chart readability result when OCR was used on the still image taken by the TPR rather than a frame from the video stream. That was caused by the fact that, though it had a front camera with ultra-high-definition sensor (3840 \(\times\) 2160 pixels), it only used its full resolution for taking still images, and generally, the robots did not stream video in full camera. Additionally, video resolution was adjusted dynamically depending on the connection quality.