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LetterEase: IMPROVING TEXT ENTRY ON A HANDHELD DEVICE VIA LETTER

REASSIGNMENT

Hokyoung Ryu*, Katrina Cruz IIMS, Massey University, New Zealand

h.ryu@massey.ac.nz*

ABSTRACT

LetterEase, a new technique for text entry on a small handheld device is described. Indeed, the current 12-button text entry keypad causes typing overhead as 26 alphabet letters are assigned to only 8 buttons (2-9). The LetterEase method uses letter-frequency data from SMS text messages collected from real situations and the moving distance between buttons with thumb use. We first discuss how to implement this LetterEase method, and then present the results of a controlled experiment comparing LetterEase to the conventional multitap method and the two-letter multitap method that was designed as a control letter assignment. The results showed that both less keystrokes and less errors using LetterEase were identified. In addition, despite slower text entry speed than that of the conventional multitap method, in effect, the users experienced LetterEase lessened the gap more readily during the course of the experiment as fast as the conventional multitap method.

KEYWORDS: multitap, keypad, input device, handheld devices

1. INTRODUCTION

Among the huge success of mobile telephone technology, Short-text Messaging Service (SMS) has been very unlikely to be so pervasive. Actually, the usability of text entry in mobile telephones with limited resources, e.g., a small number of key buttons and a small display estate, was very unlikely to attract mobile phone users; however, the lack of usability has been filled by the benefits of the SMS functionality (Ling, 2004).

Figure 1. Number-entry mode vs. Letter-entry mode in a text-editing task.

Proceedings of OZCHI 2005, Canberra, Australia. November 23 - 25, 2005.Copyright the author(s) and CHISIG

Additional copies are available at the ACM Digital Library (http://portal.acm.org/dl.cfm) or ordered from the CHISIG secretary (secretary@chisig.org)

OZCHI 2005 Proceedings ISBN: 1-59593-222-4

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Many studies of text entry in a mobile telephone, e.g., MacKenzie et al. (2001; 2002; 2003) and Ryu et al. (2002; 2005), suggested that to effectively input text on a mobile phone, at least, two issues should be considered: mode and efficiency of text input. Clearly, mobile phones employ the same action to perform various tasks. Thus, the action of pressing a particular button will lead to different effects depending on what mode the system is in. For instance, in normal calling mode pressing the button ‘2’ has the effect of putting the number ‘2’ on the display; on the other hand, when the user edits their address book, pressing the button adds the letter ‘A’ first. They are less likely to feel confused about the different effects, because the mobile phone provides a clear mode signal using the prompt in the display (e.g., See Figure 1, ‘Tel number:’ for number-entry, and ‘Name:’ for letter-entry). This mode issue has been relatively well considered in the current mobile phone design using appropriate prompts or clear mode signals, to some extent.

(a) (b)

Figure 2. (a) A conventional telephone keypad (b) a small QWERTY keypad for a handheld device

On the other hand, the second issue, which is how to effectively enter text in a mobile phone, is not so straightforward as the mode issue. This is partly because the majority of mobile phones are still equipped with the conventional 12-button keypad (see Figure 2a), even though recent handheld devices provide a small QWERTY-style keypad for text entry (see Figure 2b). Clearly, the conventional 12-button keypad bears substantial inefficiency in editing text, compared with the QWERTY keypad. However, the physical design factors of the mobile phone, e.g., size or weight, have not allowed the common use of the QWERTY keypad in a mobile phone. In effect, improving efficiency of text entry is entirely subject to how a new mobile phone design deals with the method of text entry along with the 12-button keypad. Currently, the multitap method with a 12-button keypad is the most common text-entry system for mobile phones. With this approach, the user presses each key one or more times to specify the input letter, e.g., the ‘2’ key is pressed once for the letter ‘a’, twice for ‘b’, and three times for ‘c’. As an example, ‘you’ is entered as follows:

999(y)666(o)88(u)

Naturally, the method costs substantially more keystrokes. In this example, eight keystrokes are required for three letters. An alternative way to overcome the keystroke overload is the predictive entry system with linguistic knowledge. This technique uses predictive models to automate the within-group selection, employing the use of a “next” key which allows the user to choose from a possible combination of characters, shown from the most frequent words to least frequent words. For example, to enter the word ’the’, the user enters the sequence ‘8, 4, 3’. The sequence itself has twenty seven possible combinations of letters. The system then compares these possibilities to a dictionary of words to guess the intended word. While it has provided better performance in text entry, some problems have been recently reported as text messages frequently exploit both numbers and abbreviations to make a shorter passage, which cannot be predicted by linguistic knowledge, e.g., ‘2U’ rather than ‘To you’.

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Apart from these two successful commercial designs, some researchers (e.g., Lyons et al., 2003; MacKenzie et al., 2001; MacKenzie & Soukoreff, 2002; Oniszczak & MacKenzie, 2004; Wigdor & Balakrishnan, 2003, 2004) introduced fast and efficient text input methods that are potentially available for small handheld devices. Many of these techniques demonstrated very promising results on data entry, making text input faster, more accurate and reducing the amount of keystrokes per character (KSPC). However, most of these techniques have failed to attain user’s uptake, resulting in the multitap method involving cycling through characters on a key button being the most popular.

In light of both the academic research and practical techniques, we note that the primary application of text entry in mobile phones, i.e., SMS messages, has not been clearly considered in the previous literature of text entry on small handheld devices. Remarkably, SMS messages frequently use different words against common English words, as discussed above. The difference may imply an essential design factor. In accordance with the background, this paper describes an empirical study of a newly designed text-entry method in mobile phones, finding that letter reassignment using a large corpus of SMS messages reduced overhead of the current multitap method.

2. CURRENT TEXT ENTRY ON SMALL HANDHELD DEVICES Before presenting our text entry method, the other techniques for entering text on a mobile phone are investigated in this section. Most mobile phones include a 12-button keypad similar that in Figure 2(a). The keypad consists of number keys 0-9 and two additional keys (# and *). Characters a-z are spread over keys 2-9 in alphabetic order, with space character assigned to the 0 key. Because there is a smaller number of keys than the 26 needed for the characters a-z, three or four characters are grouped on each key, and in turn ambiguity arises.

One of the approaches to text entry on a mobile phone keypad is the two-key method, involving users pressing one key to make the between-group selection, and another to make the within-group selection. For example, to enter the letter ‘t’ the user would press the ‘8’ key to indicate that was the key the letter was present on, followed by the ’1’ key to indicate that was the letter’s sequence position on the ‘8’ key. This simple technique has a KSPC of only 2, but is slower at providing text input for novices than the multitap method at approximately 5.5 wpm (Butts & Cockburn, 2001).

The multitap method is currently the most common text input method for mobile phones. MacKenzie and Soukoreff (2002) calculated the multitap method requires 2.034 KSPC when entering English text; however, it has a higher entry rate at around 7.15 wpm (Pavlovych & Stuerzlinger, 2003) than that of the two-key method.

A commercially popular text input method that rivals the multitap method is the predictive entry method that adds linguistic knowledge to the method, as discussed above. Problems arise, however, when the wrong word is suggested and the user is forced to manually cycle through the possible options via the “next” key. Prefix-based predictive entry techniques such as LetterWise (MacKenzie et al., 2001) and WordWise (www.eatoni.com) perform similarly while avoiding many of the problems just noted. Instead of using a dictionary of stored words, these methods use a stored database of probabilities of prefixes. For example, if the ‘3’ key is pressed with prefix ‘th’, the most likely next letter is ‘e’. While prefix-based predictive entry techniques improve on predictive entry methods by reducing the number of “next” key presses required, these techniques still occasionally guess the wrong letter. Predictive entry methods have a KSPC close to 1 and a mean entry speed approximately 36% faster than the multitap method (MacKenzie et al., 2001).

More recently, chording keyboards have been explored as a possible text input method for small handheld devices. A chording keyboard is one where characters are entered using combinations of key presses. ChordTap (Wigdor & Balakrishnan, 2004) operates similarly to the two-key method previously described; however “chord” keys are used instead for making the within-group selection. Twiddler keyboard, adopted for mobile phone text entry (Lyons et al., 2003), is another alternative where multiple keys are pressed simultaneously to select a character on a keypad similar to that of a mobile phone. For example, to select the letter ‘m’ the user would press the right key on the top row of the keyboard, and the middle key on the

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third row of the keyboard at the same time. Early results for both were quite promising, and showed the technique being significantly faster with less KSPC than that of the multitap method.

In spite of such promising text input methods, multitap method remains the prevailing standard on mobile phones. In effect, considering the maturity of the current mobile phone market, we found that the multitap method should be the basis of the current mobile text entry technique. Interestingly, we note Nesbat’s research (2003) to improve the current multitap method, rearranging characters according to letter-frequency data from a body of English text. That is, the most frequent letters (e.g., E, T, N, R, O, I, A, S, H) are positioned first on the keys, and the next group of frequent letters are positioned second, employing the two-key method to access them. The least frequent group of letters are positioned on one key, also employing the two-key method to select them. Results showed the method to be potentially 31% faster than the multitap method. However this has not been validated with an empirical study.

3. A NEW DESIGN: LetterEase Based on the literature discussed above, we present a new text-entry system in this section. It uses the common 12-button keypad with the multitap method; however neither the same number of characters on one button nor necessarily the same position commonly found on the current phone’s keypad. Clearly, one important principle guiding our design was the notion that different characters occur at different rates in a body of text. The other notion was to utilise the special keys, i.e., *, #, for the lettering.

3.1. LetterEase

The purpose of this study is to overcome some of multitap’s weaknesses using a letter reassignment, called LetterEase. This name was derived from the letter assignment’s primary function, namely making it easier to input letters into mobile phones. Instead of the conventional mapping of 3-4 characters per button on keys 2-9, a mapping of only 2–3 characters per button (0-9, *, #) takes place.

Oh hy! I txt last nite, bt ive gt plans 2nte neways. Hav fun tho.... Hel yeah! I finish skool @ 5, dat ok? Its normally bout 7.30 kickof, il b redy by den. Txt me details Sweet, il b dere rnd 7 U can giv me a cal Yr so wondrful sxy, i apreciate and luv u so much! Xoxo I left a msg this morn, ive alredy aceptd anotha job. I left it on yr answerng machine. It was ok, Went 2 da wariors gme on sun. It was fun xcept 4 losng @ da last minut. How was yrs? Prob nt, its nt realy my thng. Hav fun tho! Sounds gd! Text me l8r den:) I hope this is (anonymous) but yes id luv to come over one day. :-) Can we hav takeaways 2nte?

Figure 3. A fragment from 115 text messages

Apart from using the additional keys on mobile phones, the frequency of each character was reinvestigated. In fact, Mayzner and Tresselt’s data from a body of English text (1965) was very comprehensive to rearrange the current key assignment; however, the current SMS use may cause different frequency rates of each character. For this end, we randomly collected 115 SMS messages from eight participants. Figure 3 presents some excerpts from our 115 text messages. We first notice that the current text messages are commonly using both letters and numbers to compose shorter messages; furthermore, they are often abbreviated. As a consequence, we found that letter frequency would not be exactly the same as that from a body of normal English text (1965).

Table 1. Grouping of characters

Letter frequency from 115 SMS messages Letter frequency from English text First letter set e,t,o,a,n,i,s,r,u,h,y,d e,t,n,r,o,i,a,s,h,l,d,c Second letter set l,m,w,g,b,c,k,f,p,v,x,j f,u,p,m,y,w,g,v,b,x,q,k Third letter set z,q j,z

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Table 1 shows two data sets. The first column reports letter frequency from real text messages and Mayzner and Tresselt’s (1965) in the second column. Comparing the two frequency data sets, although the sequence from the two sets is not the same, the 12 most frequent letters are the same except for both ‘l’ and ‘c’ from Mayzner and Tresselt’s which is replaced with ‘y’ and ‘u’ respectively in the first column. The next 12 most frequent letters are also the same, except for ‘q’ which is swapped by ‘j’ in the first column. As ‘j’ and ‘z’ in the letter-frequency data set derived from SMS messages both hold the same frequency values, ‘z’ was chosen to remain in the least frequent category as it does in Mayzner and Tresselt’s (1965). This is because if more data was collected, ‘j’ might more likely occur more frequently than ‘z’, as it does in English text.

Following the investigation of this letter frequency, the selection of which letters are placed on which key was determined by the distance between keys. In the preliminary study, we observed that the most natural position of thumb, using 5 male adults, with both Nokia 3320 and Panasonic X60 was around button ‘1’. Therefore, using button ‘1’ as a starting point, one needs to determine how far each button is from button ‘1’ to see how far the thumb needs to travel to reach the other keys. Averaging out both a Panasonic X60 and a Nokia 3320 mobile phone, the distance data was derived as shown in Figure 4.

Figure 4. Distance from button ‘1’ (unit: mm).

In effect, the letter assignment is derived from both letter-frequency data from SMS messages collected, where the most frequently used letters are positioned first on the key and less frequently used letters are positioned second, and the distance from button ‘1’. The closer the button is to button ‘1’ the more optimal its position, as the user does not have to move their thumb as far. It is expected that this small adjustment will reduce the KSPC, ideally making text input faster and more efficient.

Figure 5. The LetterEase letter assignment

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As can be seen evident, the most frequent letters are positioned first on the key, with the next most frequent letters positioned second and the least frequent letters positioned last. Where the distance from button 1 is the same for two buttons, e.g., ‘2’ and ‘4’ in Figure 4, the button positioned directly beneath button 1 is favoured in this case because the mobile phone simulations running on PDAs in the empirical study have the buttons lined up in columns with spaces between them as shown in Figure 5. Therefore the distance between these buttons is less than the others.

3.2. Fewer Letters per Key (FLpK) As for an empirical study, we also designed the only two-letter assignment in alphabetical order that is the same as the conventional three-letter assignment on a key. Ideally, this will result in less KSPC than the conventional keypad; but probably more than that of LetterEase. This design was used to compare how much more efficient the letter-frequency data assignment really is, compared to merely distributing only two letters per key. Figure 6 illustrates the letter assignment.

Figure 6. The FLpK letter assignment in alphabetic order

4. AN EMPIRICAL STUDY To measure efficiency and task performance of text entry, we may employ the theoretical approach proposed by Mackenzie (1992) or Silfverberg et al. (2000). However, Pavlovych and Stuerzlinger’s study (2004) claimed that the predicted text entry rates tended to be overestimated with either model. Therefore, we carried out an empirical study to see how LetterEase could improve text entry.

Figure 7. The simulation program for LetterEase running on Microsoft Pocket PC emulator.

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4.1. Participants Fifteen participants aged between 21 – 53 took part in this study, who either had no experience sending SMS messages on phones in English, or little experience (less than twice a week). 12 out of 15 had no experience in editing SMS messages in English. They were all right-handed, which justifies the keypad design of LetterEase (see Section 3.1 for more details). Participants were recruited from the University in (Blind for review), and they were facilitated with a 3% course credit as an incentive to participate in the experiment.

4.2. Apparatus

An HP iPAQ Pocket PC (h6300 series) was used to run the mobile phone simulations, which was based on Microsoft Visual Studio .Net™ 2003. Three types of simulation were used in this experiment, recording each keystroke and collecting task performance data. Figure 7 shows the simulation program for LetterEase running on a Microsoft Pocket PC emulator. The messages that the participants were asked to edit appeared in the task window (see Figure 7). While the participants were typing the messages on the touch-sensitive display, they were able to see what they had typed in the display window. Naturally, the simulator provided aural feedback of each button click. Figure 8 illustrates the apparatus used in the experiment. The lack of tactile feedback of the virtual keypad was counterbalanced across three experimental conditions (MacKenzie & Oniszczak, 1998).

Figure 8. Apparatus

4.3. Experimental design

The experimental design was a one-way between-subjects factorial. System type (conventional design, FLpK, and LetterEase) served as an independent variable. Three dependent variables were taken so as to assess the effect of the system type: keystrokes per character (KSPC), word per minute (wpm), and number of errors.

4.4. Procedure

The experiment was conducted in a laboratory. On arriving at the laboratory, participants were randomly assigned into three groups, one group using the conventional one, another using the LetterEase and a third using a letter assignment with 2-3 letters assigned per key in alphabetical order (FLpK). In turn, they were provided with the instructions regarding the experiment. These gave information about the simulation, the purpose of the study, and the data protection policy. At the beginning of the experiment, the participants were asked to read the displayed text messages before entering it. Further instructions regarding how to input characters into the PDA using the multitap technique were also given by the experimenter. Additional instructions concerning space, error correction and how the experiment would progress were also given. Some previous studies reported physical discomfort obtained from thumb input after prolonged periods of time (Wigdor & Balakrishnan, 2003, 2004), so the participants were advised to rest as they like between entring messages, but to continue as quickly as possible once they started to enter a message.

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The experiment consisted of two sessions. In the first practice session, to help participants to get familiar with their system, five practice messages were entered before the main experimental session. They were selected from MacKenzie and Soukoreff (2003).

In the main session, twenty messages were entered. They were randomly selected from SMS messages collected in Section 3, which all contained similar word length. As this study is only interested in letter input, messages with numbers in them were intentionally avoided. These messages were shown to the participants on the task window of the PDA (see Figure 7), who were asked to enter text only with the thumb of the right hand that they naturally hold their phone in (see Figure 8). After performing the 10th message entry, a five minute break was given.

Timing data was collected as participants entered the first character of the phrase and finished with the last character of the message being entered. While performing the task, they were forced to correct typing errors, making the resulting text error-free. To notify typing errors, a flashing red error message was displayed at the bottom of the screen along with an auditory feedback. The participants were not able to continue entering the other text until they had corrected their mistake.

Table 2. Overall task performance in each system (20 trials)

Task performance Conditions Mean KSPC (s.d) Mean wpm (s.d) Mean errors (s.d)

Conventional three-letter design 2.10 (.37) 7.61 (2.23) 2.53 (2.32) FLpK 1.57 (.17) 6.67 (1.84) 3.74 (2.82) LetterEase 1.32 (.16) 5.49 (1.57) 2.21 (2.96)

4.5 Results Table 2 gives the means of the three dependent variables: KSPC, wpm, and number of errors. As for the keystrokes per character (KSPC), the least strokes were taken in the LetterEase condition. Looking at the mean errors, which imply accuracy while typing 20 messages, it can also be seen that much fewer errors were made in LetterEase (mean errors 2.21 per message). However, the mean word per minute (wpm), which represents the speed of typing showed the opposite direction, revealing that typing with LetterEase happened to be quite slow (mean 5.49 words per minute).

Each task performance measure was assessed by a one-way between subjects analysis of variance which revealed that there were significant differences in all three task performance measures between conditions (F (2, 277) = 14.63, p<.01 for KSPC; F (2, 277) = 27.27, p<.01 for wpm; F (2, 277) = 8.38, p<.01 for errors). These were further investigated by Tukey tests respectively. As for the KSPC, significantly more keystrokes were needed in the conventional design compared to the other two designs that were also significantly different from one another. Similarly, the Tukey test of the errors made showed that a significantly higher number of errors were made in the conventional design compared to the other two conditions that were not significantly different from one another. This result implies that LetterEase allows fewer keystrokes per character, consequently, fewer chances to make errors. However, remarkably, the speed of typing (wpm) favoured to the conventional three-letter assignment compared to the other conditions that were also significantly different from one another.

Table 3. Task performance in each ten trial

Trial 1 – 10 Trial 11 – 20 Total Conditions KSPC

(s.d) wpm (s.d)

Errors (s.d)

KSPC (s.d)

wpm (s.d)

Errors (s.d)

KSPC (s.d)

wpm (s.d)

Errors (s.d)

Conventional 2.03 (.27)

7.37 (2.26)

2.80 (2.50) 2.17

(.44) 7.85

(2.20) 2.26

(2.13) 2.10 (.37)

7.61 (2.23)

2.53 (2.32)

FLpK 1.59 (.19)

6.49 (1.91)

3.24 (2.65) 1.56

(.16) 6.84

(1.77) 4.24

(2.93) 1.57 (.17)

6.67 (1.84)

3.74 (2.82)

LetterEase 1.36 (.17)

4.06 (1.29)

2.45 (3.53) 1.27

(.12) 6.92

(1.84) 1.98

(2.27) 1.32 (.16)

5.49 (1.57)

2.21 (2.96)

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In effect, the users with LetterEase did not spot a particular character without any hesitation due to the letter assignment in disarray, whereas once they were aware of the location of the character, typing happened faster. This claim can also be supported by Table 3.

As we consider the last ten trials (trial 11 – 20), there were no differences among the three designs in terms of words per minutes (F(2, 137) = 3.01, n.s.), which represents the speed of text-entry; however the other two measures significantly indicated that LetterEase is better ( (F(2,137) = 68.27, p<.01 for KSPC; F(2,137) = 5.90, p<.01 for errors)

5. CONCLUSION AND FUTURE WORKS

One of the reasons why we chose to compare LetterEase to the two alphabetically ordered keypads was because they are used as a baseline technique in most studies of text entry techniques. In this regard, we noticed how the simple letter rearrangement had significant effects on both efficiency and accuracy. In our experiment, it is quite clear that LetterEase has some advantages over both the conventional letter assignment and FLpK. However, from the perspective of speed, it has little advantage over the other two keypad designs. This phenomenon can be explained using MacKenzie’s study of text-entry technique (2001), which divided the learning pattern of text entry into three stages: an initial discovery stage where users’ performance is significantly affected by prior experience, a second stage where users are developing their motor skills with the new design, and a third stage where expert users perform all actions reflexively (Wigdor & Balakrishnan, 2003). In our experiment, even though our users had little experience with using text entry in English, they were already aware of the location of each letter in the conventional keypad being ordered alphabetically (up to 10 trials). Once they developed their motor skills with LetterEase, they performed text entry readily fast from Trial 11.

Comparing our results to those of previous studies (e.g., Butts & Cockburn, 2001; MacKenzie et al., 2001; e.g., Pavlovych & Stuerzlinger, 2004), we see that LetterEase (6.92 wpm in the last 10 trials) is better than the two-key method (5.5 wpm) in terms of wpm, and it (1.32 in the 20 trials) is also better than the conventional multitap method (2.034) in terms of KSPC. Even through there were few errors, the error rate in LetterEase (mean 2.21) can be seen as a very low level.

While overall experimental results indicated favourability of LetterEase, the text entry method should be more refined in design and justified with other empirical studies, in that LetterEase arbitrarily used the special keys, e.g., */#, which have not been used in the conventional keypad design. We plan in future to develop a more elaborate design using the insight that we had from this study. In addition, we need to incorporate the mode issue that was discussed in the Introduction, which was not considered in the scope of the experiment. This issue may be more critical in text entry for SMS messages, in which many works (e.g., Ling, 2004) on SMS messages report different language use. The following web-site highlights the different use over common English text (http://www.funsms.net). As another avenue of future research, LetterEase may be used in conjunction with other text-entry methods to improve performance, such as the predictive entry method. However, we believe that LetterEase could potentially have a good chance of adoption, given the popular usage of the standard numeric keypad.

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MacKenzie, I. S., Kober, H., Smith, D., Jones, T., & Skepner, E. (2001). Letterwise: Prefix-based disambiguation for mobile phone text input. Paper presented at the ACM UIST, USA.

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7. ACKNOWLEGEMENT The author(s) thank the anonymous reviewers of the paper for their through comments and helpful suggestions, we are specially grateful to the members of Information Systems Group for their sincere comments of this paper.