Device Independent Text Input: A Rationale and an Example - CiteSeerX

Device Independent Text Input: A Rationale and an Example Poika Isokoski and Roope Raisamo Department of Computer Science University of Tampere P.O. Box 607 (Pinninkatu 53B) FIN-33101 Tampere, Finland Email: {poika, rr}@cs.uta.fi

1. ABSTRACT Individual characters and text are the main inputs in many computing devices. Currently there is a growing trend in developing small portable devices like mobile phones, personal digital assistants, GPS-navigators, and two-way pagers. Unfortunately these portable computing devices have different user interfaces and therefore the task of text input takes many forms. The user, who in the future is likely to have several of these devices, has to learn several text input methods. We argue that there is a need for a universal text input method. A method like this would work on a wide range of interface technologies and allow the user to transfer his or her writing skill without device-specific training. To show that device independent text input is possible, we present a candidate for a device independent text entry method that supports skill transfer between different devices. A limited longitudinal study was conducted to achieve a proof of concept evaluation of our Minimal Device Independent Text Input Method (MDITIM). We found MDITIM writing skill acquired with a touchpad to work almost equally well on mouse, trackball, joystick and keyboard without any additional training. Our test group reached on average 41% of their handwriting speed by the end of the tenth 30-minute training session.

Keywords device independence, MDITIM, text input, minimalism, portable devices, unistrokes

2. INTRODUCTION Even though graphical user interfaces have become a standard and speech recognition has been researched for a long time, writing is still the most common way to enter data in a computing device. In fact, when we nowadays discuss writing we often refer to writing with computer. Therefore, the method that we use in entering text in a computer is a crucial part of a user interface. Currently there is a growing trend in developing small portable devices, like mobile phones and personal digital assistants. A

QWERTY keyboard is the most common way to enter text, but it is not feasible in these small appliances. Most of these devices are handheld and have some kind of primary input device, like a small keyboard, a touchpad with a stylus, or a small joystick. Desktop computers can also be equipped with many different input devices that have both common and specialized features. In the last few years there has been a great increase in supply of different input technologies. Especially various devices that enable mouse-like interaction have been introduced. Usually different devices are used in different tasks for which they have been designed. However, if we needed to use these devices for text input there is no universal method that we could use. If text input is possible at all, each device has its own input method that needs to be learned. In this paper, we argue for device independent text entry methods and give an example of one such method. We present a longitudinal study to evaluate our Minimal Device Independent Text Input Method (MDITIM). MDITIM writing skill was found to be device independent, but the speed and accuracy varied with different devices. We tested the method with touchpad, mouse, trackball, joystick and keyboard. The average writing speeds for our five test subjects on these devices were between 7.6 wpm (touchpad) and 4.9 wpm (keyboard). The error rates varied between 3% (joystick) and 7.2% (trackball). When these speeds are compared to device dependent input methods, we notice that MDITIM is slower. Thus, we do not propose that MDITIM would be used as the only text input method, unless it is the only option in some specialized device. We believe that this kind of device independent method could be added in many computing devices as an alternative text input method. Our method is very robust and forgiving in respect to timing and direction that the writing movements are made. This paper is organized as follows. First, we present previous work that is related to our method along with commentary on the adaptability on various input technologies. Next, the minimal device independent text input method is explained in detail. Then, we describe a longitudinal study that was carried out to analyze this method, and we report on its results. Finally, we discuss our findings and give suggestions for further research.

3. PREVIOUS WORK AND DEVICE INDEPENDENCE Methods that allow text to be entered into technical devices have been researched ever since the typewriter was first developed. The QWERTY keyboard, while not the optimal solution, has become the standard in entering text in computers. QWERTY keyboard gained its status as an input device for computers

through easy skill transfer from typewriting. Now we have become so accustomed with QWERTY keyboards that it is not probable that they will disappear any time soon. Optimized layouts, such as Dvorak [5], have not been widely accepted because they would require additional training and do not offer large enough improvements in speed and accuracy. Both the wide acceptance of QWERTY keyboard and the ignorance of Dvorak keyboard emphasize the importance of easy skill transfer between devices and the reluctance of users to learn multiple writing methods. Keyboards are at their best as physical objects. They can give rich tactile feedback to the writer. Soft-keyboards which are keyboard layouts shown on a touch-sensitive display have also proven to be usable despite the lack of tactile feedback. In softkeyboards the QWERTY layout has been shown to be clearly inferior to layouts especially designed for soft-keyboards [13, 14]. However, most users are familiar with the QWERTY layout and layouts with greater speed-potential show their strength only after lengthy practice [13]. The QWERTY layout as an abstraction has proven to be usable even with an interface consisting of a character selection scheme with only five buttons and a small 4-line display [2]. However, for example on mobile phones the QWERTY layout has not been popular. For historical reasons mobile phones have different keyboards and text-entry methods [14, 24]. In many cases these methods outperform QWERTY layout based selection techniques simply because the phones and the displays on them do not have enough room for the QWERTY layout. Thus QWERTY and keyboard layouts with similar number of keys work well as physical objects and as softkeyboards, but that is the extent of their device independence. Furthermore touch-typing skill, which is the best reason for using keyboards, is layout specific. Thus keyboards support skill transfer between interface technologies only when the key arrangement is not changed much [14]. A set of writing techniques has been developed especially for pen-based interfaces. This set includes methods like Goldberg and Richardson’s Unistrokes [9], Graffiti [1], Perlin’s Quikwriting [20], Venolia and Neiberg’s T-Qube [25], and traditional on-line handwriting recognizers [23]. The strength of these methods is that they utilize the very fine control that many people have over a stylus tip. This is also the weakness of these methods when it comes to device independence. The association between stylus movements and characters cannot generally be transferred to other interface technologies in a usable form. A possible exception is the Quikwriting system, which in fact extracts pairs of tokens from the pen input. These pairs are directly mapped to characters and other functions. One can imagine that Quikwriting could be used with a small keyboard typical to mobile phones. Speech technology is sometimes mentioned as a universal means for communicating with computing devices. Speech technology, both recognition and synthesis, is currently on a level that does work within limitations [21]. Both recognition and synthesis are generally language specific and the best recognition accuracy is achieved with speaker specific training. We must notice that speech is tightly coupled with one interface technology. Speech does not work without microphones and loudspeakers. In general, various methods for text input do exist and the range covers almost all input devices. The leading design goal has been to optimize performance with a certain input technology. In

recent years this has indeed been a valid primary goal. The manufacturers of various computing devices do want to stake claim for specific market niches and a good way to do that is to design a device with an user interface that outperforms the competition in that part of the market. As the market develops and people own several of these devices, the priority of design goals starts to change. Many people interact with several different computing devices daily. Interoperability becomes more important. This means that the devices must be able to interchange data and that the users must be able to use the devices with minimal effort. In a situation like this interaction techniques that may not be the best option for a given interface technology, but that perform reasonably well on many technologies become valuable. The use of the QWERTY layout on soft-keyboards is an example of this kind of development. Our understanding is that in near future a situation arises where people have many computing devices without a full sized keyboard. This motivates the learning of one slightly complicated and device independent writing method rather than several somewhat faster and easier ones. One tradeoff in the design of interaction techniques is between device-specific performance and the range of devices that the technique is usable at all. At the extremes we get either optimal performance on just one user interface technology or relatively low performance on a very large range of user interfaces. As explained above, we believe that the device independent side is becoming more important. The remainder of this article describes the design and evaluation of one candidate for a device independent text input method.

4. A DEVICE INDEPENDENT TEXT INPUT METHOD As described above the design of a truly device independent text input method allows the use of only those characteristics that are common to all input devices. To ensure compatibility with as many of the currently unknown future devices as possible we must minimize the set of features that we want to use in our writing method. Thus, we choose minimalism as our primary design guide. With minimalism we must set the limit somewhere. We chose to set it in five different input tokens. This is enough to allow us to use traditional two-dimensional geometric shapes to describe the characters. We hope that this makes the method easier to learn and faster to use than, for example, the more constrained Morse code, which has not gained much popularity as a text input method in modern computing devices. Goldberg and Richardson [9] argue that unistrokes have a longer learning path than regular handwriting recognition giving a touch-typing equivalent for pen based text entry. However, with Goldberg and Richardson’s unistrokes the learning path ends at the character level. The writer must lift the pen between characters. Our goal was to improve this situation and allow word-level unistrokes as had been done at least twice before [18, 20]. Because we can separate the screen representation of the text and the input that is used to enter it, the input does not have to be human legible. This means that characters can be written on top of each other as noted by Goldberg and Richardson [9]. We can use any manual movement or other physical activity to write text.

The Minimal Device Independent Text Input Method (MDITIM) allows characters to be written in parts, with one stroke per character, or in groups as a single (albeit slightly complicated) stroke, as seen in Figure 1. Figure 1 shows the MDITIM direction strings with arrows and mouse-paths that will generate the direction strings. The word “cab” is an example of a word-level unistroke written with MDITIM. A stroke begins from the circle. The basic input unit is one of four principal directions. We have named the directions after their counterparts in the compass as North, East, South, and West or N, E, S, and W for brevity. The characters are assembled from these directions so that the same direction never appears twice in a row. Thus, NN, EE, SS, and WW are forbidden combinations within MDITIM characters. This limitation disambiguates situations where one direction is drawn once or twice. If, for example, two N directions are drawn with a single stroke, we would not be able to tell where the first one ends and the second begins. This potential ambiguity only exists on devices that deal with drawing and strokes in the first place. Such devices, however, are rather numerous and a solution is in order.

Clearly creating a MDITIM character set where the intercharacter ambiguities do not appear is possible, but it disallows so many direction strings, that the input sequences that are associated with characters grow in length. Another possible way of handling this situation is to choose the characters so that intercharacter repeating directions are very rare. This is the case with the NSWWES (ae) string in our experimental character set. The ae-combination is rare in the English language. The third way to solve the problem is to require user action to distinguish between the two consecutive directions. This is the way we chose to solve the problem in our experimental software. We used a 70-millisecond timer. When 70 ms had passed without any input from the user, the recognition algorithm would accept the previous direction again. If the previous direction had completed a character, the second incarnation would start a new one. Otherwise the situation would mean that the user entered an invalid MDITIM code. We used invalid codes as signals that caused the current direction string to be forgotten. This gave the user the possibility to “undo” the current character if he or she noticed that it was a wrong character or that an error had occurred while writing it.

4.1 Properties of MDITIM The input described above is very simple. The advantage that we gain from using only four simple building blocks is that almost any input device (other than a simple button or a slider) allows the entry of at least four different entities. These entities can be mapped to the four MDITIM directions and we can use all these devices for text input. Because we are only interested in the four principal directions, MDITIM should be very robust when used on a pen-based interface. Right angles such as SE and NW can be drawn as arcs and possible excess length of the strokes does not confuse the recognition algorithm. The goal of designing MDITIM to be device independent made the recognition algorithm much simpler than the algorithms that were used in original Unistrokes [9] and Graffiti [1]. Figure 1. Idealized MDITIM characters and possible real world strokes. MDITIM characters are prefix codes (see [6] or any algorithm book for details) formed with the (N, E, S, W) alphabet. In practice, this means that if we have a character associated with the direction string NSW, we cannot have NSW appearing in the beginning of a direction string associated with any other character. Thus we can, without any ambiguity, extract characters from a string of MDITIM directions or alternatively deduce that the string is not valid MDITIM input. While the direction string is unambiguous, that is not always the case with the actual input. While we do not allow repeating directions within characters, we do allow a character to end with the same direction that another character begins with. Thus given two characters NSW and WES we notice that the direction string NSWWES, has a repeating W in the middle. This causes ambiguity if the user chooses to write these two characters with a single stroke. We do not have a simple way of knowing where the first westward stroke ends and the second begins. Different solutions to this problem are described next.

Optimal prefix codes [6] are a traditional data compression method. MDITIM alphabet can be constructed to minimize direction usage. On the other hand it may not be wise to emphasize the compression too much because, as noted by Goldberg and Richardson [9], slightly simpler characters (or in our case shorter codes) do not necessarily mean significantly faster writing. MDITIM, like all unistroke character sets, has Goldberg’s and Richardson’s three good qualities of unistrokes: (1) only a little writing area is needed (a little more for word-level unistrokes), (2) eyes free operation is possible, and (3) the method is easier for the wrist than traditional pen and paper writing (only finger movement needed).

4.2 Details on the Experimental Character Set The MDITIM character set we chose for our experiment was based on two main principles. First, we attempted to maintain optimal direction usage, which means assigning frequent characters slightly shorter codes. Second, we made exceptions to the first principle in order to give some characters forms that resemble the characters of the Roman alphabet. It is very difficult to make all characters similar to the Roman alphabet with only four directions. This means that some

characters may be difficult to learn for people familiar with the Roman alphabet. The choice of the characters that got a familiar form was somewhat arbitrary. Whenever a convenient form was available we used it. This means that we cannot claim that the resulting character set is the best possible, but it also gives us an opportunity to study the effect that different degrees of familiarity have on the learning rate of the characters. We examined three samples of texts to find the most frequent characters. The first one was the Gutenberg etext collection available at http://www.gutenberg.org. The second was the Linux kernel source code shipped with RedHat Linux 5.2 and the third was a sample gathered by Mayzner and Tresselt [19], which has later been amended by Soukoreff and MacKenzie [22] to include the space character. The first two samples contained upper and lower case characters along with punctuation and other common symbols. Soukoreff and MacKenzie gave numbers for only the 26 letters of the English alphabet and space making no difference between upper and lower case letters. We did optimize our character set for character frequencies, and Roman alphabet resemblance, but we did not try to avoid intercharacter repeating directions. Neither did we give any consideration to the writing space requirements of the most frequent character combinations. These two issues are valid questions to consider in MDITIM character set design if we want to allow easy word-level unistroke writing. The resulting character set is shown in Table 1. In total, there are 58 individual direction strings giving 96 individual characters if we count both upper and lower case characters. We see that two characters (space and backspace) can be written with only two directions. Space is usually the most frequent character. Our samples contained between 14.6% and 18.7% spaces while the next frequent character “e” got numbers between 5.4% and 10.8%. We wanted to give backspace a simple and fast form to facilitate corrections. If making corrections is too difficult the users may judge the writing method cumbersome and unappealing to use. NSW SEW ESW SWE WES ESNE ESNS WSWS WNS SESW WSWE SNS WSWN NSN WSEN EWEN EWEW NE EWNE EWNW EWSW SNWS

Char a b c d e f g h i j k l m n o . ? space ( { ‘

Mod A B C D E F G H I J K L M N O : ? space + ) } “

Direction WNEN WSES WSN ESE SNE SEN WNWS WNWN SWSN SWSE SWSW WEN NSE WNES

Char p q r s t u v w x y z å ä ö

Mod P Q R S T U V W X Y Z Å Ä Ö

EWES EWSN NW EWNS EWSE SNWE SNWN

, ! bs * < [ @

; ! bs / > ] @

SESN WNEW SWN ENEN ENES ENEW ENSN ENSE

| $ return 1 2 3 4 5

| $ return 1 2 3 4 5

WNWE WEWE

= &

= &

ENSW ENWN ENWE ENWS ESNW

6 7 8 9 0

6 7 8 9 0

Table 1. MDITIM direction strings. To keep the codes short we devised a modifier scheme for writing upper case characters. The modifier may have different forms with different input devices, but the idea is to add something to the input if the direction string is to be interpreted as an upper case letter. In our experiment we used a button press as a modifier. A button pressed at any time during the writing of a character caused an upper-case character to be written. Keeping the button down while writing had an effect similar to the “Caps Lock” key found in most traditional keyboards. The “mod” columns in Table 1 show the effect of the modifier. The character set is currently usable with many Western languages. To make the character set expansible we reserved a level three branch for two special letters in the Finnish alphabet, letters Ä and Å. They act as place holders for more numerous forms of different characters that are required in other languages, as in French or Spanish. We do not intend the current character set to be the final word in MDITIM character sets. However, we did not find any obvious faults in it when it was used to enter English text.

4.3 EXPERIMENT To evaluate our method we conducted a longitudinal empirical test. Our goal was to learn whether MDITIM can be used for text input, and if so, to get an estimate for the learning rate and initial performance. We recruited five unpaid volunteers from the staff of our university. Two of the subjects were female and three male at ages between 23 and 29. Each user carried out ten separate training sessions. We tried to schedule the ten sessions to consecutive days, but we also had to accommodate weekends, summer holidays and some unexpected changes. The result was a schedule where there were no more than three days, but at least five hours between sessions. During the first session, handwriting and QWERTY-typing speeds were measured for all subjects. The first session ended with the first of the ten 30-minute practice sessions using MDITIM on a handheld touchpad. Sessions 2-9 had only the 30minute practice period. Finally session 10 started with the 30minute practice session and ended with the measurement of MDITIM writing speed and error rate using different input devices. The users completed a 5-minute writing session with mouse, trackball, joystick and keyboard (Figure 2). The order of the devices was altered between the subjects to avoid bias.

The writers were allowed to rest between phrases, but were especially urged to complete each phrase without interruptions. The users were also asked to correct all errors if it could be done without erasing a lot of text. Users generally judged more than a word to be “a lot”. However the resulting text was relatively error free as most errors were spotted before completing the word. In all tests the users were instructed to write as fast as they could while making as few errors as possible.

Figure 2. The different input devices that were used in the experiment. On the keyboard we used the arrow keys and shift for a modifier. With all other devices the modifier was the button designed for primary use. The handwriting speed was measured using an A5 checkered notepad and a ballpoint pen. Text was shown on a computer screen and the subjects were instructed to keep the pad on the table. QWERTY-typing speed was measured in a similar task. Instead of writing on a notepad, the subjects wrote to another window on the computer screen. For MDITIM writing we constructed a piece of software (Figure 3). The same software was used in the practice sessions and in the 5-minute tests on the four devices. The program showed a short phrase and the subject was expected to write the phrase using MDITIM. The progress of the writing could be observed on screen. The phrase written so far was shown below the original phrase. In addition, a major part of the screen was reserved to display the state of the recognition algorithm. Whenever a direction was detected, it was drawn on the screen. The test subjects could thus visually track their actions. A “click” sound was also played when a character was completed.

We used the same set of phrases in all our testing. The phrases were extracted from the fortune cookie database delivered with Red Hat Linux 5.2. All 451 phrases with less than 30 characters were extracted and placed in a file. The beginning of this file was used in the handwriting and typing tests. The MDITIM writing program selected phrases randomly from the file. The collection of phrases consisted mainly of regular English such as: “Have a nice diurnal anomaly”, but also contained some more challenging writing tasks, such as: “E = MC ** 2 +- 3db”.

5. RESULTS 5.1 Speed Speed of writing is a very central property of a writing method. We measured the speed of our writers throughout the experiment. The writing speed varied greatly depending on the type of text that was written and the writer’s alertness. We computed writing speeds within time windows of varying lengths. With a 10-second window we found that the speed varied between 0 wpm to 20 wpm. With a one-minute window the speed stayed mostly within 2 wpm range from the average. With even longer windows the variation decreased even further as we expected. Thus, a couple of minutes of writing is likely to give a good estimate of a person’s writing speed. Because the writers could rest between phrases, we were unable to accurately measure the writing speed of the first character in each phrase. Therefore, these characters were excluded from the speed computations. Figure 4 shows the mean speeds for each of our five writers during the experiment. Writers 1 and 3 were more than twice as fast as writer 4. A finding that explains the slow speed of writer 4 is that she did twice as many mid-character undo operations as the other writers on average. The writing speeds in Figure 4 and in the whole paper are given as words per minute (wpm). One word per minute in this paper is equal to five characters per minute including spaces, punctuation and generally everything that is written.

productive speed (wpm)

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Figure 3. MDITIM testing prototype. The user is working on an “M”.

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Figure 4. Mean productive speeds for all subjects.

5.2 Error Rate The speeds shown in Figure 4 are productive speeds. This means that all errors and input needed to correct them are excluded. Figure 5 shows the difference between raw and productive writing speeds. The curves in Figure 5 are arithmetic means of the individual writing speeds of our test subjects. Figure 5 also shows an estimate for the peak error free writing speed that has also been used in reports on similar writing methods [9]. We computed the peak rate by finding the fastest writing time for each character and then weighing each time with the frequency of the character. The curve in Figure 5 is the arithmetic mean of the peak error free writing speeds for each of our five writers.

16

speed (wpm)

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Figure 6 shows the percentage of written characters that were wrong. The error rate reaches 6% by the fourth session and gets values on both sides of 6% thereafter. In other words the error rate quickly decreases to certain level and does not change with further practice. The same phenomenon has been observed in other studies [7, 16]. We were strict in counting the errors. Every character that was not exactly the correct character was counted as an error. For example, the user could write an upper case letter "A" even though it should have been "a". In these cases (25% of all errors encountered in the experiment) the main input would have been correct, but the modifier key caused the error. Because all other studies do not give details on the handling of upper and lower case characters [9, 18, 20], we cannot accurately compare our results. If we left out these case-sensitive errors the average error level over the whole experiment would have been 4,6%. This is close to what has been observed in similar tasks [12].

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Prediction Raw

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Figure 5. Mean raw speed, productive speed and upper bound prediction over all sessions. Some researchers [25] have used medians for representing the writing speeds. This may be wise, because the distribution of writing speeds for a given character tends to be clustered close to zero with a long tail towards the positive infinity. The reason for this is that some characters get written rather slowly because the writers let their thoughts to wander, or some external factors affect their concentration. We found that with our data using medians would have given slightly higher speed estimate, but we still opted to use arithmetic means to express the averages because the slowly written characters do deserve attention. They may be exceptions, but if the number of very slow exceptions grows too large, there may be something wrong with the writing method. During the experiment we observed that the writers quickly started writing with character level unistrokes. They found this easier than lifting their finger between every direction. However, they did not advance to writing with word-level unistrokes. There are at least two potential reasons for this. (1) It may not have occurred to them that word-level unistrokes are possible. (2) The touchpad could have been too small and word-level unistrokes would have been difficult to write. One of the test subjects confirmed that both reasons were true for him. Figures 4 and 5 clearly show that the writing speed is still growing in a steady rate during the tenth session. An even longer study can be carried out, but it would take years to give the test group the same amount of experience in a new technique as they have with regular handwriting and QWERTY typing.

error rate

2 10 % 8% 6% 4% 2% 0% 1

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Figure 6. Mean error rate over all sessions.

5.3 Device Independence To see whether MDITIM writing skill transfers to other devices, we measured the above-mentioned statistics for four additional input devices. Figure 7 gives the writing speeds for each of our five writers with each of the devices. The numbers for the touchpad are the results of the last 30-minute practice session. The other results were measured in a five-minute test to capture only the initial performance with a new device.

correlate across writing methods. The correlation coefficient between handwriting and typing speeds was r=0.41. Handwriting and MDITIM writing speeds had a slightly stronger correlation at r=0.65. Typing and MDITIM writing speeds had the strongest correlation with r=0.87.

productive speed (wpm)

12 10 8

On average the writers reached 28% of their QWERTY typing speed and 41% of their handwriting speed during the last 30minute training session.

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5.4 DISCUSSION

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Figure 7. MDITIM writing speeds with various devices. We see in Figure 7 how both within subject and between subjects writing speeds vary greatly. Some test subjects were slightly faster with other devices than with the familiar touchpad. They definitely transferred their writing skill and used it well with a device that was for some reason better suited for them than the original touchpad. The error rates on the different devices were equally divergent as seen in Figure 8. This may be partially due to the relatively short test. One error in five minutes is often enough to make onepercent difference in the error rate. The average results are slightly more reliable and show that keyboard and joystick were relatively error free at rates around 3%, while touchpad and trackball were more error prone with rates over 6%. The short test may favor the new devices, because the test subjects had compulsory rest periods between the five-minute tests. It may be easier to hold concentration for five minutes than for half an hour.

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Figure 8. MDITIM writing error percentages on various devices. The handwriting speeds that we measured in the beginning of the experiment ranged between 11 and 29 wpm. The QWERTY typing speeds were between 14 and 40 wpm. Surprisingly two of our test subjects were slightly faster at handwriting than in QWERTY typing. While our sample was too small for a reliable statistical analysis, we found that the writing speeds seem to

Unifying interaction techniques is a direct opposite to the trend of developing more task specific interaction techniques and devices. Many efficient text input methods are tightly coupled with certain input devices such as chord keyboards [10] and touch sensitive pads or displays [9, 20]. We have presented MDITIM as an example of device independent text input methods. The test subjects received a lot of visual feedback during the writing experiment. This may have hindered very fast writing simply because writing methods that require constant visual feedback are slower than those that manage with tactile feedback [4] and still slower than those that can be used without constant feedback. More importantly the experiment did not reveal whether MDITIM can be learned or used without visual feedback. Eyes-free operation will probably cause greater variations in speed and error rate between devices. With touchpad, mouse, or trackball it is difficult for the user to know whether the algorithm has recognized a direction. With joystick or keyboard the user gets better tactile feedback because the manual tasks are discrete and clear, and the user can thus track the state of the recognition process more easily. One possibility for providing better non-visual feedback is to add tactile feedback mechanisms to the devices that normally lack them. Tactile feedback has already been used in other fields at least with mice [11] and touchpads [15]. Another possibility is to use continuous auditory feedback. Several studies have shown that auditory feedback is beneficial in user interfaces [3, 8]. In our review of the previous work, we found that, it is difficult to compare studies that report on different text input methods. There are differences in the amount of characters used, the way the errors are defined and the way the speed is reported. Some studies [9, 10, 14, 16, 25] used a smaller character set, which results in a smaller number of separate input gestures, and thus the methods were easier learn and use. All studies have not tested both upper and lower case letters, which further simplifies the task and makes it different from the real writing task. We found differences in speed and error rate when different input devices were used for text input. In the last session some users performed better with the other devices than with the already familiar touchpad. The differences that existed between the devices follow the earlier results that have compared input devices in pointing and dragging tasks. Specifically, [17] compared mouse, touch tablet and trackball. The accuracy of these three devices followed the same order as the error rate in our results (see Figure 8). It is clear that even if MDITIM is device independent in that the writing skill is transfers between the devices it can not remove the innate differences that exist in different kinds of input devices.

The fact that MDITIM requires an initial learning effort from a writer can be seen as a feature common to all device independent text input methods. We cannot rely on techniques like characters printed on top of keys or menus shown on displays to make the method accessible to novices. This is because we cannot rely on that all devices have a keyboard or a display.

6. CONCLUSIONS According to our results MDITIM is not fast enough to challenge current text input in speed competition during the first five hours of practice. Although the writing speed will increase with further practice, it seems likely that MDITIM is not as fast as the fastest device dependent writing methods. This was expected since the method only uses input device features that are common to many different devices. This inevitably leaves many possible features of the input out of reach and does not use many devices optimally. Still, we believe that MDITIM can be useful if the alternative is to learn more than two other writing methods. In general, a device independent input method is useful for users that need to use many different devices. It gives them a common way to write text with any device, and the users can learn a more efficient device dependent input method over time if they continue to use the device.

7. ACKNOWLEDGMENTS We wish to thank the five volunteer test subjects for making our experiment possible.

8. REFERENCES 1. 3Com Corporation / Palm Computing. Graffiti. http://palm.3com.com/products/input/index.html 2. Bellman, T. and MacKenzie, I. S. A probabilistic character layout strategy for mobile text entry. Proceedings of Graphics Interface '98, Canadian Information Processing Society, 1998, 168-176. 3. Brewster, S. Providing a Structural Method for Integrating Non-Speech Audio Into Human-Computer Interfaces. Ph.D. dissertation, University of York, UK, 1994. 4. Card, S.K., Moran, T.P., and Newell, A. The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates, 1983. 5. Cassingham, R.C. The Dvorak Keyboard. Freelance Communications, 1986. 6. Cormen, T.H., Leiserson, C. E., Rivest, R. L. Introduction to Algorithms, MIT Press, 1996. 7. Frankish, C., Hull, R., and Morgan, P. Recognition accuracy and user acceptance of pen interfaces. Human Factors In Computing Systems, CHI ’95 Conference Proceedings, ACM Press, 1995, 503-510. 8. Gaver, W. Auditory icons: using sound in computer interfaces. Human-Computer Interaction, 2, 1986, 167177. 9. Goldberg, D. and Richardson, C. Touch-typing with a stylus. Human Factors in Computing Systems,

INTERCHI '93 Conference Proceedings, ACM Press, 1993, 80-87. 10. Gopher, D. and Raij, D. Typing on a two-handed chord keyboard: will QWERTY become obsolete? IEEE Transactions on Systems, Man, and Cybernetics, 18, 1988, 601-609. 11. Hughes. R.G. and Forrest, A.R. Perceptualisation using a tactile mouse. IEEE Visualization '96, IEEE Computer Society, 1996, 181-188. 12. Kurtenbach, G. and Buxton, W. The limits of expert performance using hierarchic marking menus. Human Factors in Computing Systems, INTERCHI '93 Conference Proceedings, ACM Press, 1993, 482-487. 13. MacKenzie, I. S. and Zhang, S. Z. The design and evaluation of a high performance soft keyboard. Proceedings of the CHI 99 Conference on Human Factors in Computing Systems: the CHI is the limit. ACM, 1999, 25-31. 14. MacKenzie, I. S., Zhang, S. X. and Soukoreff, R. W. Text entry using soft keyboards. Behaviour & Information Technology, 18 (4), 1999, 235-244. 15. MacKenzie, I.S. and Oniszczak, A. A comparison of three selection techniques for touchpads. Human Factors in Computing Systems, CHI '98 Conference Proceedings, ACM Press, 1998, 336-343. 16. MacKenzie, I.S., Chang, L. A performance comparison of two handwriting recognizers. Interacting with Computers, 11, 1999, 283-297. 17. MacKenzie, I.S., Sellen, A., and Buxton, W. A comparison of input devices in elemental pointing and dragging tasks. Human Factors in Computing Systems, CHI ’91 Conference Proceedings, ACM Press, 1991, 161-166. 18. Mankoff, J. and Abowd, G.D. Cirrin: a word-level unistroke keyboard for pen input. ACM UIST’98 Symposium on User Interface Software and Technology, ACM Press, 1998, 213-214. 19. Mayzner, M.S. and Tresselt, M.E. Tables of singleletter and digram frequency counts for various wordlength and letter-position combination. Psychonomic Monograph Supplements, 1, 1965, 13-32. 20. Perlin K. Quikwriting: continuous stylus-based text entry. ACM UIST’98 Symposium on User Interface Software and Technology, ACM Press, 1998, 215-216. 21. Rudnicky A. I., Hauptmann A. G. and Lee K. Survey of current speech technology, Communications of the ACM, 37 (3) 1994, 52–57. 22. Soukoreff, R.W. and MacKenzie, I.S. Theoretical upper and lower bounds on typing speed using a stylus and soft keyboard. Behaviour and Information Technology, 14 (6), 1995, 370-379.

23. Tappert, C.C., Suen, C.Y., and Wakahara, T. The state of the art in on-line handwriting recognition. IEEE Transactions on Pattern Analysis and Artificial Intelligence. 12 (8), 1994, 787-808. 24. Tegic Communications, Inc. T9 text input. http://www.tegic.com.

25. Venolia D. and Neiberg, F. T-Cube: A fast selfdisclosing pen-based alphabet. Human Factors in Computing Systems, CHI '94 Conference Proceedings, ACM Press, 1994, 265-270.