###### Abstract  
The COVID-19 outbreak demonstrated the viability of various remote working solutions, telepresence robots (TPRs) being one of them. High-quality video transmission is one of the cornerstones of using such solutions, as most of the information about the environment is acquired through vision. This study aims to compare the camera capabilities of four models of popular telepresence robots using compact reduced LogMAR and Snellen optometry charts as well as text displayed on a projector screen. The symbols from the images are extracted using the Google Vision OCR (Optical Character Recognition) software, and the results of the recognition are compared with the symbols on the charts. Double 3 TPR provides the best quality images of optometric charts, but the OCR results of measurements of the image on the projector do not show the clear advantage of one single model over the others. The results demonstrated by Temi 2 and Double 3 TPRs are generally better than the others, suggesting that these TPRs are more feasible to be used in teaching and learning scenarios. tepresence robot; telepresence; robotic telepresence; vision; vision acuity; education; optometry +
Footnote †: journal: Medical  
rates. Some authors highlight the need to consider both technical and user-related factors in the assessment [7]. One of the key characteristics that affect the usability of a TPR is its \"eyesight\" or the quality of video that it streams. The main factor that determines the quality of video is the camera used by the robot; however, this can be affected by other factors, including the network connection, image processing algorithms, and so on. Comparing the image quality of video streams or still images requires a common scale. In addition, as a substantial amount of information in the education domain is delivered using slides, it is logical to test the operator's ability to read such text. Optometrists use a variety of charts to test and evaluate a person's eyesight. These charts are used to determine a person's visual acuity, which is a measure of how clearly a person can see, and we believe that the same method can be used to estimate and compare the quality of the robot's video subsystem. LogMAR (Logarithm of the Minimum Angle of Resolution) and Snellen are among the most popular systems used to measure visual acuity, or the clarity of vision [8]. The Snellen chart, invented in 1862 by a Dutch ophthalmologist named [PERSON], is the most widely recognized eye chart [9] and is used to measure visual acuity from 20 feet. It consists of rows of letters that are progressively smaller as one moves down the chart. The smallest line of letters that a person can read accurately is used to determine their visual acuity, which is expressed as a fraction, with the top number (numerator) representing the distance at which the test was taken (20 feet or approximately 6 m) and the bottom number (denominator) representing the distance at which a person with normal vision can read the line. For example, 20/20 vision means that a person can see at 20 feet what a person with normal vision can see at the same distance [10]. The LogMAR chart, introduced in 1976, on the other hand, is a more advanced system [11]. It uses a logarithmic scale to express visual acuity, with a higher score indicating worse visual acuity. The chart consists of letters that are progressively smaller as one moves down the chart, and the smallest line of letters that a person can read accurately is used to determine their visual acuity; therefore, LogMAR charts have replaced Snellen charts in ETDRS (Early Treatment of Diabetic Retinopathy Study) standard tests [11]. Overall, the LogMAR system is considered more accurate and precise than the Snellen system [3], as it considers the size and contrast of the letters on the chart and is not affected by the distance at which the test is taken. However, the Snellen chart is still widely used due to its simplicity and ease of use.