A novel approach to study the effect of font and background color combinations on the text recognition efficiency on LCDs
AA novel approach to study the effect of font/background color combinations on the text’s recognition efficiency on LCDs
Zeliang Cheng + , Vahab Vahdat + , Yingzi Lin + * + Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA * Corresponding Author email: [email protected]
Abstract
With the popularization of cell phones, laptops, and tablets, Liquid Crystal Displays (LCDs) have become one of the main types of User Interface (UI) in the modern world. While LCDs are widely used for retrieving text information, the impact of text formatting on the legibility is often overlooked. With the goal of improving recognition efficiency (RE) on LCDs, this paper studies the impact of font/background colors on RE of texts being presented on LCD. For this purpose, difference between font/background color combinations, Primary Color Difference (PCD), is introduced that brings efficient RE assessment under wider spectrum. Accordingly, a testing platform is designed in C
Keywords:
Human Computer Interaction, User Interface, Recognition Efficiency, displays, text color
1. Introduction
User interface (UI) is a media that handles the interactions between human and machines where the UIs are widely divided into physical and computerized interfaces [1, 2, 3]. In recent years, the use of personal electronic devices such as laptops, cell phones, and tablets have become more prevalent than ever. A study in 2010 shows that US students spend more than 20 hours per week using cellphones only for texting and calling [4]. Moreover, with the rise of technology, computers and other electronic devices have being widely used as a displaying, controlling, modifying, and monitoring terminals in many industries from healthcare to nuclear power plants the distributed ontrol systems[5]. For all of the electronic devices a human-computer interface (HCI) is used as the primary user interface that act as a bridge between the machine and human. Typical HCIs can vary from simple indicator lights, buzzers, or speakers, to more complex Liquid Crystal Displays (LCDs), Light Emitting Diode (LED) displays, or any combinations between them. Among current HCIs, electronic displays such as the LCDs are the most commonly used HCI in various machines and devices being used by majority of people everyday [6]. While the use of electronic devices have soared in last few years, the importance of HCI readability is often overlooked. For instance, many healthcare institutes and hospitals are using electronic health record (EHR) and Electronic Medical Records (EMR) systems to store and retrieve patients’ medical history and information [7]. EHR systems are widely adopted in recent years transitioning from paper-based documentations to a computer-based media [8]. EHR and EMR resulted to a growing number of information being presented on LCDs in the health care operations. A proper design of the way information being displayed on LCDs (e.g., the background, font size, font type, and font color) could decrease the human errors for information retrieval and prevent fatal misjudgments [9]. With a general goal of improving the human-computer interaction efficiency, this paper studies the relationship between the visual performance of LCDs and the text presentation legibility, to be specific, the font and background color combination. One important indication of visual performance of certain text presented on LCDs is its Recognition Efficiency (RE)[10]. The text’s RE is further related to its readability or legibility. Readability is the reader’s ability to recognize the form of a word or a group of words for contextual purposes[11], while the legibility is the reader’s ability to recognize the form of single word without contextual purposes. In a common sense, the more legible words or texts are being presented, the higher their RE is. In this paper, we examine legibility of texts for different sets of font and background colors with series of non-meaningful and randomly composed three-letter word stimuli for eliminating the learning effect of the participants. Unlike previous studies that only experimented a limited number of font and background color, in order to assess a wider range of font and background color combinations, Primary Color Difference (PCD) is utilized. Using PCD, we are able to generalize our results while preserving our design of experiment framework relatively feasible. he rest of the paper is organized as follows: In section 2, a review of literature regarding recognition efficiency, color definitions in LCDs, and font/background color assessments for both reflectors and luminophor materials are provided. In section 3, the primary color difference method and the underlying hypotheses of the research are further explained. Experimental design steps, preparation, setup, and protocols are discussed in section 4, followed by descriptive and statistical data analysis using analysis of variance and response surface method. In section 6, discussion of the research highlights, insights, and primary outcomes are further explored. Finally, the conclusion remarks and recommendations for future research are summarized in Section 7.
2. Background
Table 1
Comparison of Le Courier and Humar legibility table Font Color BK G R B W BK Y W W W R G R Background Color Y W W W B W BK R G BK Y R G Le Courier Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 Humar Ranking 7 5 1 10 6 4 11 8 3 13 2 9 12
Note: “R”= Red, “G”= Green, “B”= Blue, “BK”= Black, “W” =White, and “Y” =Yellow. Finally, the methods used by previous researchers can be classified into three main categories with respect to measurement of the participant’s visual performance[6] [Humar et al., 2008]. The first method is called visual search tasks [20, 51, 52]. One example of this method is asking participants to either identify misspelled words or find special words from non-meaningful paragraphs. The second method is to measure the recognition time for certain stimuli (characters, words or sentences) [18, 29, 42, 53]. The third method is to measure participant’s accuracy in words recognition tasks. The stimuli were shown to the participants either for a very short time or in a relatively small font size and the participants were asked to identify as many as possible stimulus during the task [41, 54, 55]. The first and third methods, namely visual search tasks and word recognition have similar realizations with the fact that the participant memory retention, previous experience, and instantaneous concentration have important impact on the results. As a result, in this study, we have adopted to use the second method by measuring the recognition time for each stimulus, striving the decrease participants’ characteristics impacts and reducing the risks of skewed results, which will be elaborated in the following parts. Our general goal is to find a more efficient way to examine the effect of the font/background color combinations on the RE and to ell from statistical perspective which color combination is better than the other.
3. Methods
4. Data Collection and Experimental Design
Figure 1
The interface of constructed stimuli for capturing participant response time
Stimulus words combinations were selected in a way that participants could not easily guess the stimulus word unless he or she recognizes all its three letters. For instance, two of the nine stimuli start with letter “R”, two of them start with letter “S”; two of the nine stimuli end with letter “P”, two of them end with letter “T”. igure 2
Experiment process using the designed computer program. The black squares represent the LCD display at each stage of the experiment
Prior to performing any experimental design, careful considerations of factors that might affect test results are inevitable[57]. Environmental conditions, experiment conditions, and instruments
InitializeShow only the background for 1000msShow crosshair on the background for 200msShow only backgroundagain for 200msShow the font stimulusand begin timingStimulus disappear whenthe subject pressed the Space buttom. Stop timingTest finished?Test finishedYESNO
APR ccurateness are examples of conditions and factors that should be controlled. Environmental factors such as light intensity and audible noise were stabilized during the experiments by controlling the laboratory conditions. The experiment instruments, screen size, screen type, screen resolution, computer hardware specifications, keyboard type, chair and table type, and height kept similar for all participants. In order to select LCD displays’ type and specifications, most prevalent displays’ settings were considered. LCD displays with 1920×1080 resolution and screen refresh rate of 60Hz, diagonal viewing Size of 24 inches with preset display area (H×V): 20.9×11.7 square inches were used during the experiments. The laboratory conditions such as distance of participants to the display, display’s angle, position of display, and keyboard were consistently controlled. In order to verify the relative position between participants and the display, all participants were required to sit straight comfortably on an adjustable rotary chair in front of the display. The subjects were also required to adjust the display and the chair properly that make sure their sight lines are as perpendicular to the center of the display plane as possible. The ergonomic designs suggest 50 to 70 centimeters distance between eyes and screen and 10 to 20 centimeters lower than user eyesight. Participants were given the chance to get familiar with the testing software and the procedure before the test.
Each test takes between 5 to 10 seconds and the whole testing per each participant (180 experiments) takes between 20 to 40 minutes. After each 60 experiments, articipants were asked for a 10 minute break before proceeding to the next wave of experiments. In order to decrease the impact of participant’s fatigue on the results, the sequence of stimuli that is tested for each participant is not the same. For instance, while participant 1 may have been tested black background and ΔR=110 (test st , 15 th , 45 th , 87 th , 90 th , 128 th , 151 th , 169 th , 177 th , and 178 th experiment. Therefore, the fatigue impact is distributed evenly among all stimuli. Table 2
Design of experiment tests. Combination of font and background color resulted 18 tests. Each test is repeated 10 times by each individual. test
Black White ∆ Red ∆ Green ∆ Blue 110 180 250 110 180 250 110 180 250 1 1 0 1 0 0 0 0 0 0 0 0 2 1 0 0 1 0 0 0 0 0 0 0 3 1 0 0 0 1 0 0 0 0 0 0 4 1 0 0 0 0 1 0 0 0 0 0 5 1 0 0 0 0 0 1 0 0 0 0 6 1 0 0 0 0 0 0 1 0 0 0 7 1 0 0 0 0 0 0 0 1 0 0 8 1 0 0 0 0 0 0 0 0 1 0 9 1 0 0 0 0 0 0 0 0 0 1 10 0 1 1 0 0 0 0 0 0 0 0 11 0 1 0 1 0 0 0 0 0 0 0 12 0 1 0 0 1 0 0 0 0 0 0 13 0 1 0 0 0 1 0 0 0 0 0 14 0 1 0 0 0 0 1 0 0 0 0 15 0 1 0 0 0 0 0 1 0 0 0 16 0 1 0 0 0 0 0 0 1 0 0 17 0 1 0 0 0 0 0 0 0 1 0 18 0 1 0 0 0 0 0 0 0 0 1
According to Fisher et al. [58], any experiments conducted under statistical Design of Experiments (DoE) framework have to follow three principals (i.e. randomization, replication, and blocking) in order to achieve a reasonable and valid finding [58, 59]. Randomization is the process f assigning individuals randomly to one or group of experiments. In our experiments the order of allocating font/background color was randomized. A proper randomization approach helps “averaging-out” impacts of extraneous factors that might present. Replication decrease impacts of variability by repeating each factor combination independently. It also helps obtaining an approximation of experimental error. For this purpose, the experiment for each font/background combination was repeated 10 times although the actual text stimuli were different during the replication process. Finally, blocking, a design technique that arranges experimental units into a group to improve comparison precision and eliminate known variation, was implemented. We used blocking to group results by participants. Since participants may have faster/slower reactions to the same stimuli in comparison to others, by grouping each participant, a set of relatively homogenous experimental conditions were compared.
5. Data Analysis
By the time that all participants completed the experiments, 1980 response time dataset corresponding to fractional factorial design are collected. In order to analyze the dataset, descriptive analysis, Factorial Analyses of Variances (ANOVA), and Response Surface Methods (RSM) are conducted to compare response time to each combination of font/background color. All analysis was performed with Minitab 15 and R-Studio. ANOVA results revealed that not only the main factors (i.e. font and background color) have significant effects on text’s legibility, interactions between factors do too. 5.1 Descriptive Analysis The average response time and the standard deviation for each experiment is provided in Table 3, based on the test numbers (Test able 3
Mean response time and Standard deviation (in milliseconds) for each test case.
Test
In order to get more in depth analysis from the experiments dataset, box-plot analysis for each color is provided in Figure 3. Each color is grouped based on the ∆ values into 250, 180, and 100. According to the figures, the green font color outperforms red and blue, both in terms of median response times and inter-quartile range. The blue font color, as oppose to the green, has a high response time and the difference between values of ∆ (∆=250, ∆=180, ∆=110) is remarkably more than the red and the green.
Figure 3
Box-plot analysis of font color response time in milliseconds. The response times are grouped ased on the ∆ values.
Similar analysis has performed on the background colors (black and white), as shown in Figure 4, providing histogram and box plot, accordingly. Based on the results, majority of response times for black background falls between 200 to 400 milliseconds with few responds higher than 1 second. In contrary, the white background has a smoother distribution and most of the response times is between 100 to 500 milliseconds, but number of responds that take 1 second or more, is notably higher than black background. igure 4
Box-plot and histogram analysis of background color response time in milliseconds.
While the analysis provide useful insights about the performance of font and background colors, the interaction effects between different combination of font and background colors are not emphasized. For this purpose, in Section 5.2, ANOVA results pertaining to interactions between the factors of the design of experiment is provided. 5.2 Statistical analysis for main factors
Font/background colors as the main factors of our factorial design framework were compared by Factorial Analyses of Variances (ANOVA). There was a significant difference between two background colors (ANOVA, F= 911.55, p = 0.00) as well as between different font colors (ANOVA, F= 3385.06, p = 0.00). In addition to main factors, ∆ values were compared. Through grouping font colors by their ∆ values (i.e. ∆=250, ∆=180, ∆=110) rather than ∆Rs, ∆Gs, and ∆Bs we were able to further express impacts of ∆ values on the legibility. Results show that there was a significant difference between ∆ values (ANOVA, F= 263.50, p = 0.00), though F-value was comparatively lower. However, the corresponding R-Sq was high (R-Sq = 83.28%) to assure the data was fitted well to ANOVA statistical model. Figure 5 depicts mean response time (in milliseconds) for each factor. Green had least response time followed by red and blue. Also response time to black background color was less than white.
Figure 5
Mean response time (in milliseconds) for each font color, background color, and the difference (∆)
It has been confirmed that the interactions between the factors of an experiment make response value (here RT) among levels of different factors to be different [58, 59]. In other words, interaction is the failure of one factor to generate same impacts on the response value at different levels of another factor. Hence, we analyzed all possible interactions of factors to interpret significant changes in response time. Figure 6 illustrates all possible interactions among levels of font and background
350 550 R E S P O N S E T I M E ( M I LL I S E C O N D S ) BACKGROUND COLORS550 450 350 R E S P O N S E T I M E ( M I LL I S E C O N D S ) ∆ VALUES780 250 350 R E S P O N S E T I M E ( M I LL I S E C O N D S ) FONT COLORS olors, and ∆ considering its values as an independent factor. The interaction plot between background and font color is shown at the top right. At the experiments that the font color was green, the response time was similar regardless of background color, but when the font color was Blue or red, the interaction between factors led to different response time when levels of one factor changed. The interactions between ∆ values and font colors are shown in middle right. In all delta values, green font color had least response time following by red and blue respectively. ∆ values and font colors had not significant interaction and the previous trend were remained unchanged. Also, there was not a significant interaction between background color and ∆ values on response time as shown in middle top of the Figure 6.
Figure 6
Pairwise Interactions impacts between font color, background color, and ∆ on
Response time (in milliseconds).
While the design of experiment results provide useful insights about the impact of ont/background color on RT and how the interaction between these colors can affect the recognition efficiency, it is only limited to the colors provided. In order to generalize the results, a method that furnish the continuous color spectrum rather than given discrete points (i.e. 110, 180, 250) were required. Response Surface Methodology (RSM) provides deeper explorations among associated control variables to one or more response of interest. In general, RSM includes of group of statistical and mathematical techniques used in building an unknown functional relationship between response variables and a number of input variables. Fitting response curve to the levels of a factor can be beneficial to interpret that relationship, predict response value with different combinations of design factors, and determine the optimum setting of variables to minimize (or maximize) response value. Figure 7 shows how changing font colors and ∆ values concurrently, might affect response time for each background. Note that minimum response time achieved when font color is between green and red. When the font color is blue, by increasing ∆ value, the response time can be decreased consistently while in black background, but ∆ values does not impact the recognition efficiency of blue in white background. While in the black background, the best response time area achieved by ∆ values above 235, the best response time in white background corresponds to ∆ values between 220 to 250 intervals. Additionally, the black background provides a more consistent platform where for the majority of font and ∆ combinations, the response time is lower than 300 milliseconds. On the contrary, the white background recognition efficiency is more sensitive to the font and ∆ combinations, resulting higher response time with small modifications to the combination of font and ∆. This is a good expansion of our results since ∆ values as well as font colors can be inferred as continues variables.
Figure 7 Response Time contour plots for each background color based on ∆ and font colors. Font colors codes are 1= Green, 2= red, 3=Blue.
6. Discussions
Results has proven that the background/font color combination has a significant effect on the RE. Based on the results, black background outperforms the white background in terms of the RE. Moreover, when maximizing the ΔG value the RE could be greatly increased, but in the similar level the impact of ΔR is relatively small, with inverse impact of ΔB while at the same level. Thus the effect of PCD on the RE can be shown as “ΔG>ΔR>ΔB”. Moreover, font/background color combinations with a higher PCD value of either ΔG, ΔR or ΔB shows a better performance of the RE compared with those color combinations with a less PCD value. According to the provided statistical analysis, the font color, background color, and PCD values, as well as the interactions between these factors have significant effect on the Recognition Time (RT). Some of the research outcomes can be intuitively shown in Figure 8 (in example text “Hello”). n this example graph, each row indicates a certain PCD value (ΔR, ΔG and ΔB of either Δ=250, 180 or 110). The left half of the graph is on black background and the right half is on white background. According to our conclusions, the text “Hello” on black background is generally more distinguishable than that on white. Besides, in general, texts with its ΔG maximized are more distinguishable than those with ΔR maximized or those with ΔB maximized. For each of the three PCD groups in the graph, the texts on the first row are more distinguishable than the texts on the following rows while the upper group (ΔG) shows a more clear of such trend (ΔG>ΔR>ΔB). Font with the ΔG maximized with Δ=250 is most distinguishable while font with the ΔB maximized with Δ=110 is least distinguishable. These subjective intuitions is in accordance with our statistical results.
Figure 8
An illustration to primary color difference of font/background colors with two backgrounds (black and white) and three levels of difference (250, 180, 110), in the green, red, and blue respectively. For instance when (ΔG=250), the PCD provide a green color on black screen and purple color in white screen.
One inference from the conclusion is that the RE of the text on certain background colors can be maximized when increasing both the ΔG and the ΔR simultaneously but further research is required that confirms this hypothesis with more color combinations. . Conclusions and Future Research
In this study, the effect of font/background color combination on the Recognition Efficiency (RE) is examined. One of the contributions of this study is using a new concept of Primary Color Difference (PCD) between the two colors in studying the virtual performance of the user interface. Unlike prior studies such as Humar [6] and Zhao et al.[35] that verifies the legibility of limited colors, we examined a wider color combination spectrum using PCD. Most importantly, this study offers a realizable way to maximizing the RE for any given color combination. After reviewing the pertaining literature, we conducted the experiments with a reduced full factorial design with 2 factors: background colors with 2 levels (White and Black), and font colors with 9 PCD values between a given background color and the font colors (ΔR=250/180/110, ΔG=250/180/110, ΔB=250/180/110). Results has proven that the background/font color combination has a significant effect on the RE. The results of this study can be easily extended to prior research. For instance, Zhao et al.[35] indicated that yellow could be a reasonable choice of background color when the font color is black font for a text on computer screen being magnified and presented to the readers. The yellow color code is (255, 255, 0) and the black color code is (0, 0, 0). Therefore, the PCD is (255, 255, 0). Our results shows that in order to increase recognition efficiency, “ΔG>ΔR>ΔB” should be maximized, which is in accordance with the yellow background and black font color (ΔG=255, ΔR=255, ΔB=0). Similar to any human factor studies, this study comes with some limitations. First, all of the participants were students between age of 19-28 years old and the number of participants were limited due to the timeframe of the project. In order to overcome this limitation, each participants performed every test for 10 times, but more participants with broader range of age is sought for further research, ensuring the consistency of results. Recent studies show that reading performance and pointing movements in touchscreens for elderly people achieved lower comprehension in comparison with young people [60, 61, 62]. Rather than static text, with the rise of online courses and electronic educations, a high number of videos that contain texts have been used in recent years. The text legibility, while considering font and background colors can also address with the future research [15]. Moreover, in this study the value of ΔR, ΔG, and ΔB are examined individually without considering the consequences that two or three of the PCD values change concurrently. Future research can address changing the value of green, red, and blue simultaneously and provide new understandings of font/background color combinations. Along with font/background color combination, other color properties such as saturation, tone, intensity, and hue may impact the recognition efficiency that providing a general framework to assess the importance of all font properties, would be beneficial.
Acknowledgements:
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