Technical ReportPeer-Reviewed

Inputting Writing Systems with Medium Complexity:
A Generalized Input Method Editor AKKHARA
and Case Study on Myanmar Script
Chenchen Ding† , Masao Utiyama† and Eiichiro Sumita†
In this study, an input method editor called AKKHARA is developed to accommodate
writing systems comprising several tens to hundreds of symbols. As an engineering
realization, AKKHARA accepts and applies a set of rewrite rules with priorities suchthat the alternation, substitution, and normalization of character strings are appliedalongside the keystrokes. Compared with general key-character editors, AKKHARA provides a greater ﬂexibility for Romanization-based rule editions. Compared with the
input methods developed for Chinese and Japanese, AKKHARA is lightweight and easy
to maintain. As an application case of AKKHARA, this study illustrates the realizationof a Romanization-based Myanmar input method using the Unicode standard. A version of AKKHARA for Microsoft Windows was released that supports Unicode characterswith customizable functions for rewriting rule editions.
Key Words: Abugida writing systems, Input method editor, Myanmar script

1 Introduction
Various alphabet writing systems have been used to record numerous languages worldwide.
These alphabet systems use tens of distinguishable symbols to transcribe phonemes. Conversely,
languages, such as Chinese and Japanese, apply logogram systems totally or partially in theirorthographies, thus requiring thousands of symbols to represent morphemes.
This study focuses on a group of syllabic systems that lie between the aforementioned extremes. In this report, they are referred to as having a medium complexity. Compared withlogogram systems, they are still based on phonetics, although a redundancy in spelling may
be retained to distinguish morphemes; compared with an alphabet system, they assign symbols
based on syllables rather than phonemes, and may apply a nonlinear combination to characters.
Traditionally, these syllabic systems are input by direct keystrokes and treated as common
alphabets. Because of the larger symbol set, the keyboard layout is crowded and requires one or† Advanced Translation Technology Laboratory, Advanced Speech Translation Research and Development Pro-

motion Center, National Institute of Information and Communications Technology.

(C) The Association for Natural Language Processing. Licensed under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

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Inputting Writing Systems with Medium Complexity

more alternating keys. However, these systems are not as complex as those used in Chinese or
Japanese scripts, wherein language-dependent input methods are indispensable. Therefore, an
input editor that is 1) lightweight, 2) language-independent, and 3) editable and maintainable,
is expected to eﬃciently input such writing systems.
Motivated by these requirements, the AKKHARA editor developed in this study provides a
general solution. AKKHARA repeatedly applies rewrite operations to convert keystrokes into speciﬁccharacter strings. Therefore, compared with a keyboard layout editor, AKKHARA has a greater
ability and ﬂexibility in applying string operations than simply appending new characters using
keystrokes. This is important for writing systems with certain redundancies in their encoding
systems, because some complex combined glyphs can be realized in more than one ways. Thus,
the rewrite operations can normalize the input to generate consistently encoded textual data.
Temporary input methods for Chinese and Japanese scripts are typically based on common
Romanization systems, such as Pinyin and Rōmaji. However, numerous languages that do not
use the Latin alphabet lack a consistent Romanization system. In AKKHARA, a set of rewrite rules
is organized in an editable textual ﬁle in a structured format, thereby enabling users to customize
the input method according to their preferences.
Predictions based on words, phrases, or even sentences, supported by large-scale data, are acommon function provided by Chinese and Japanese input methods. Ding et al. (2018) investigated an auto-complete method for several abugida systems that can achieve a high accuracy,even with limited training data. This is a direction for the future development of AKKHARA.
AKKHARA primarily provides a layer for the local decisive conversion between raw keystrokes andwriting systems for users to generate correctly coded and normalized strings. Based on this layer,
sophisticated functions, such as context-based prediction can be further developed.
The remainder of this study is organized as follows. Section 2 introduces the linguistic background of writing systems. Section 3 reviews related works and applications. Section 4 provides
a general overview and instructions for rewriting rule editions to deﬁne an input method on arbitrary character sets. To illustrate the application of AKKHARA, an input method of the Myanmar
script (Ding et al. 2019) is used as a case study. Section 5 reviews the original design of the Myanmar input method and describes AKKHARA’s more extendable realization. Section 6 concludes thereport and presents ideas for future work.

2 Linguistic Background
Table 1 lists the main types of writing systems used worldwide, with a comparison of the1255

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number of symbols applied. Generally, an analytic phonetic writing system, e.g., a typical alphabet or abjad system, contains approximately 30 (≈ 101.5 ) symbols. This is because the averagephonetic inventory of natural languages contains 22 ± 3 consonants (Maddieson 2013a) and ﬁve
or six vowels (Maddieson 2013b). By contrast, a logogram system may contain thousands ofsymbols for daily use. For example, the Table of General Standard Chinese Characters1 contains
8, 105 characters for Chinese, and the List of Jōyō Kanji2 contains 2,136 characters for Japanese.
Figure 1 compares character distributions across diﬀerent writing systems. Parallel datafrom the Asian Language Treebank (Riza et al. 2016)3 were used. The diﬀerences between an

Type
LogogramPhonogram– syllabic– abugida– alphabet– abjad# Symbol> 103

ExampleChinese character

101.5 –103
101.5 –103
≈ 101.5≈ 101.5

Japanese kana
Devanagari script
Latin script
Arabic script

Table 1

Types of writing systems and the scale of symbols used in them. This report refers to theabugida/syllabic systems as having medium complexity.

Fig. 1

Character distribution of Latin letters in English, the kana and kanji in Japanese, and the four
abugida systems of Myanmar, Khmer, Thai, and Lao. Frequency is the normalized relative
frequency, i.e., empirical probability; the rank is by the descending order of the frequency.

1 http://www.gov.cn/gzdt/att/att/site1/20130819/tygfhzb.pdf
2 https://www.bunka.go.jp/kokugo_nihongo/sisaku/joho/joho/kijun/naikaku/pdf/joyokanjihyo_

20101130.pdf
3 https://att-astrec.nict.go.jp/member/mutiyama/ALT/

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alphabet system (English), a mixed syllabic and logogram system (Japanese), and four abugida
systems (Myanmar, Khmer, Thai, and Lao) are presented. Evidently, the four abugida systems
with medium complexity share similar features, but diﬀer from the alphabet/logogram systems.
Generally, these medium complexity systems contain more commonly used symbols and a longer
tail of obscure characters compared with general alphabet systems. However, their complexity is
still far from that of a logogram system.
For scripts supported by the Unicode Basic Multilingual Plane, the following are taken as theobjects of AKKHARA, where the number of points assigned by Unicode is provided in parentheses.
Most scripts are abugidas; herein the dagger symbol indicates that the script is syllabic.
Devanagari (128 ), Bengali (96 ), Gurmukhi (80 ), Gujarati (91 ), Oriya (91 ), Tamil (72 ), Telugu (98 ), Kannada (89 ), Malayalam (118 ), Sinhala (91 ), Thai (87 ), Lao (82 ), Tibetan (211 ),Myanmar (160 ), Ethiopic (358 ), Cherokee† (92 ), Uniﬁed Canadian Aboriginal Syllabics4 (640 ),Khmer (114 ), Limbu (68 ), New Tai Lue (83 ), Tai Tham (127 ), Balinese (121 ), Sundanese (66 ),Batak (56 ), Lepcha (74 ), Vai† (300 ), Bamum† (88 ), Saurashtra (82 ), Javanese (91 ), Cham (83 ),Tai Viet (72 ), and Meetei Mayek (56 ).
Herein, systems with lesser complexity, such as Tai Le (35 ) were excluded. For brevity, only
the names of the scripts and number of assigned points are listed. One script may be used in
diﬀerent writing systems,5 wherein only a portion of the symbols is used, or extended symbols
are required.

3 Related Work and Applications
Popular operating systems (OS) such as Microsoft Windows and Macintosh provide keyboard
layouts for various writing systems. Several keyboard layout editors, such as Keyman,6 have beendeveloped for more sophisticated layout editions. These input editors focus on key-charactermapping with extensions for handling contextually dependent keystrokes.
For writing systems using Chinese characters, the RIME7 input method engine has customization functions that allow users to design input schemes. This project supports obscure ancient
fonts and dialect-based input methods. Regarding the medium complexity scripts of interest in
this study, the RIME conﬁguration is excessively complex.

4 Essentially Abugida but encoded as syllabary.
5 For example, the Devanagari is used by the languages of Hindi, Marathi, Sindhi, Nepali, and Sanskrit.
6 https://keyman.com/
7 https://github.com/rime

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Ding et al. (2019) proposed an input method for Myanmar script that can be formulated using
a complex automaton. Rather than key-character mapping, the basic idea concerns converting
strings such that operations, such as looped alternations, substitutions for combined glyphs,
and normalizations for ambiguities and redundancies, in Unicode can be modeled in a uniform
framework. However, this method is applicable only to the particular writing system, and theoverall automaton is hard-coded. AKKHARA follows the basic idea of the speciﬁc input method
and extends it to a language-independent input editor.

4 Functions of AKKHARA
4.1

Interface

AKKHARA can be installed and used as a common input editor for Windows.8 It supports multiple
input methods that can be selected from a list. Figure 2 shows the launch screen of AKKHARA, andFig. 3 shows an image of typed and converted strings. For a selected input method, the details
can be customized and viewed in the interface, as shown in Fig. 4.

4.2

FormatIn the background, an input method is deﬁned using a text ﬁle named *.akkhara, where thesymbol * denotes the name of the input method. Each line of the ﬁle is organized in the following
format.

Fig. 2

Interface launching AKKHARA. Four scripts are listed: Khmer, Laos, Myanmar (selected), andThai.

Fig. 3

Input interface. Pressing the space bar inputs the bottom converted Myanmar script. Pressing
the enter key inputs the top original string.

8 https://att-astrec.nict.go.jp/member/ding/my-akkhara.html

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Fig. 4

Inputting Writing Systems with Medium Complexity

Interface showing details of the input method selected. The left screen shows a customization
wherein speciﬁc rules are enabled/disabled; the right screen lists all of rules enabled.

input ||| output ||| priority ||| status,(1)

where, input and output are the Unicode strings before and after a rewriting operation, respectively. priority is a non-negative integer, and rules with smaller values are always applied
before rules with larger values. These rules are for local operations and applied ad-hoc after
each keystroke. priority can also take a value of −1 for a global ﬁnal normalization after allother operations have been conducted. status is assigned a value of 0, 1, or −1. The valuesof 1 and −1 are ﬂags for the rules enabled and disabled by customization, respectively, and 0
denotes rules that are always applied.
The rewriting operations can actually describe a regular language because they always rewrite
one side of a string with the input processing.9 Therefore, AKKHARA can realize any input method
as long as the required conversions can be formulated by a regular language. Note that priority
is unnecessary if the rules are deliberately organized and strictly operated in a predeﬁned order.
priority can help users group rules for editing and maintenance. Rules that do not require
explicit orders can be assigned with identical priority.
An akkhara ﬁle can be created and edited by a general text editor, such as Notepad on

9 Rules with a priority of -1 conduct an overall rewriting on a string, which is a more powerful operation thana regular language. This is provided only to handle certain intrinsic encoding problems in Unicode, whichshould not bother users.

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Windows. For the input and output ﬁelds in a rule, ASCII characters10 can be used directly;
other characters should be represented using hexadecimal Unicode. The rules should be integrated
by the increasing values of priority from 0, ending with those with a priority of −1. A reboot
is required to reload an akkhara ﬁle after modiﬁcation.
The following subsections provide general descriptions of editing the rules for realizing alternations, substitutions, and normalizations of character strings. The descriptions omit the statusof the rules as this depends on user preference.

4.3

Simple Mapping

For basic key-character mapping, simple rules can be described, as follows.
a1 ||| u1 · · · un ||| 0 |||,

(2)

where a1 is an ASCII character available on a general US keyboard; and u1 · · · un is a Unicode
string composed of one or more characters to be input by the keystroke of a1 .
For these basic mappings that directly use ASCII characters, priority is generally set to 0,
because no other prior rules are applied.

4.4

Simple Conversion

Rules (3)–(5) provide a typical example for realizing a simple conversion function. Using two
keystrokes, a1 and a2 , u3 can be input. Note that the keystrokes of a1 and a2 have their own
input functions and that u3 is generated by a rule with a priority of 1. This guarantees that
Rule (5) executes after Rule (4).
a1 ||| u1 ||| 0 |||,

(3)

a2 ||| u2 ||| 0 |||,

(4)

u1 u2 ||| u3 ||| 1 |||,

(5)This type of rule is suitable for characters with stable digraph Latin transcriptions, such as
using h for aspiration in many systems. In cases where a digraph may cause ambiguities or whereusers may intentionally disable the conversion, the following rules can be added as solutions.
a0 ||| 200B ||| 0 |||,

(6)

200B u2 ||| u2 ||| − 1 |||,

(7)

10 ASCII characters here are letters and marks that can be directly input by keystrokes on a US keyboard.

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Here, a0 inputs an invisible separator by Rule (6), and the zero-width space of 200B is deleted
by Rule (7) as a ﬁnal normalization with a priority of −1. Subsequently, a sequence of keystrokes
a1 a0 a2 forcibly inputs the sequence u1 u2 .
In practice, the conversion key a2 and separator key a0 can be customized based on the
Romanization preference, frequency of ambiguities, and keystroke distributions.

4.5

Conversion by Multiple Keystrokes

As a natural extension of simple conversion, a conversion key can be used multiple times toconvert characters through a series. Generally, Rules (3)–(5) can be further extended using thefollowing rules. Using these rules, uk+1 can be converted by one keystroke of a1 , followed by k − 1
keystrokes of a2 .
u3 u2 ||| u4 ||| 1 |||,

(8)

···
uk u2 ||| uk+1 ||| 1 |||,

(9)

···
Note that the priority values from Rule (8) can be set to 1. As the rewrite is implemented
after each keystroke, the order of these rules does not need to be distinguished. When one isapplied, the previous rewrite has already been completed.
Conversion using multiple keystrokes is suitable for a series of redundant characters for thesame phoneme in a speciﬁc writing system, where only one or two of them are frequently used.
Therefore, the inputs of remaining obscure ones can be grouped by the sequential conversion.

4.6

Looped Conversion

As another type of conversion, a single keystroke can be repeated to input a group of charactersin a looped manner. Rules (10)–(12) realize a function in which a series of keystrokes a1 , u1 andu2 can be converted in a looped manner. That is, an odd number of keystroke(s) of a1 can input
u1 , and even number for u2 .
a1 ||| u1 ||| 0 |||,

(10)

u1 u1 ||| u2 ||| 1 |||,

(11)

u2 u1 ||| u1 ||| 1 |||,

(12)

Similarly, by replacing Rule (12) with the following rules, looping can be extended to an

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u2 u1 ||| u3 ||| 1 |||,

(13)

arbitrary number of characters.

···
uk−1 u1 ||| uk ||| 1 |||,

(14)

uk u1 ||| u1 ||| 1 |||,

(15)

This type of looped conversion can be used for a set of symbols (mostly two or three in practice)that share identical Romanization by one letter, such that they can be quickly converted using
identical keystrokes.

4.7

Normalization of Variants

In some writing systems, context-dependent variants of speciﬁc symbols are used. If u1 andu′1 are two variants of one character, while encoded separately by Unicode, then the following
rules realize an example of context-dependent conversions.
u2 u1 ||| u2 u′1 ||| 2 |||,

(16)

u3 u2 u′1 ||| u3 u2 u1 ||| 3 |||,

(17)

By Rule (16), when u1 comes after u2 , it is substituted by u′1 . When u1 comes after a stringof u3 u2 , it is kept as it is, which is further supported by Rule (17). Note that the priority ofsuch normalization rules begins from 2 to distinguish them from general input and conversion
operations. The longer the context, the larger the priority assigned. In the most extreme case,certain obscure variants may appear only in a few speciﬁc words. For such rules, a large constantof priority can be set for convenience.

5 Case Study
5.1

General Issues and a Myanmar Input Method

In general, the following issues are required to design a minimum scheme to input a writing
system with medium complexity.
• The arrangement of some common characters. These characters should be input by simple
keystrokes.
• The arrangement of other characters that require alternation keys. The alternation key is
primarily the shift-key, or a second alternation key is required if characters remain.

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Inputting Writing Systems with Medium Complexity

The original work of Ding et al. (2019) focused primarily on these two issues, thus providing
a basic interface for inputting the speciﬁc set of characters. Their work extended the use ofalternation key(s) for more eﬃcient conversion operations. The instruction of the input method
can be printed by users on A4 paper (Fig. 5, i.e., Fig. 4 in the original paper). The conventionallyaccepted Romanization, frequency of characters, and keystroke distribution on a QWERTY keyboard
are considered in the design for eﬃciency.
In brief, the Myanmar Language Commission (MLC) Transcription System (Department of
the Myanmar Language Commission 2014) was applied. Because the MLC system is rigid andacademic, it is simpliﬁed for input purposes. For example, aspirated consonants are denoted bya preceding h in the MLC system such as hk or hp, for historical reasons. This is adjusted tosucceeding h, such as kh and ph to input the corresponding letters. Compared with the MLC
system, this design is based on general use of Latin letters to address the conversion function using
h in the aforementioned example. In addition, vowel systems are simpliﬁed such that more than
one letters with similar phonetic values are arranged on one key, and obscure letters can be input
using multiple keystrokes of the same key. For other diacritic marks, those frequently used arearranged in the middle area of the keyboard (e.g., on the f and j keys) for easy keystrokes. Moredetails about the design, as well as about the keystroke distribution compared with traditional
keyboard layout, can be found in the original paper (Ding et al. 2019).
For further convenience, an input method should consider orthographic and encoding issues
because:• Some redundant characters are context-dependent. In some cases, they are encoded as
one character by Unicode and the variants are accommodated by font design. In othercases, they are treated as diﬀerent characters that require manual selection.
• The combination of some complex glyphs can be realized in more than one manner; theorder of component characters is not strictly deﬁned. Similarly, speciﬁc fonts may besensitive or insensitive to diversity.
These issues require detailed normalization operations, which require users to have a goodunderstanding of the encoding and fonts when everything is performed manually. The original
Myanmar input method was formulated using a ﬁnite-state automaton (Hopcroft et al. 2013) andhard-coded to satisfy the minimum input requirements. More sophisticated operations are notexplicitly mentioned as these functions are, although possible, diﬃcult to realize using a vanillaautomaton. Therefore, the original implementation is more illustrative than practical.
The following subsection discusses the realization of the input method using a generalized
AKKHARA with basic extended normalization operations.
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Each cell provides the Unicode, Myanmar character, and input manner top to bottom. For Myanmar characters with more than one
input method, a vertical bar is used to separate the diﬀerent input manners.

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5.2

Inputting Writing Systems with Medium Complexity

Realization Using AKKHARA

An akkhara ﬁle that realizes the aforementioned input method was released with the AKKHARA
binary ﬁles. The Appendix lists rules organized in Tables 2, 3, 4, 5, and 6, according to theirpriority of 0, 1, 2, 3, and −1, respectively. The leading number is the line number in thereleased ﬁle.
Table 2 includes the basic mapping described in Sec. 4.3 for two punctuation marks, 10
digits,11 and 26 lower/uppercase Latin letters. The three letters a, o, and x have a one-to-many
mapping. Table 3 lists further conversions. The table is divided into three blocks from top tobottom and from left to right. The ﬁrst block (lines 65–95) contains simple conversions (mainly by
103E and 1002, and by 103B and 103C), as described in Sec. 4.4; the second block (lines 96–105)
contains looped conversions for ﬁve pairs (102D/102E, 102F/1030, 1031/1032, 1038/1037, and
103A/1039), as described in Sec. 4.6; and the third block (lines 106–110) contains ﬁve conversions
implemented using multiple keystrokes, as described in Sec. 4.5. Tables 2 and 3 cover the schemein Fig. 5, which realizes the minimum requirements for basic character conversion.
From Table 4, the normalization functions in Sec. 4.7 are applied. Tables 4 and 5 concern
the normalization of the variants of 102C/102B. Character sequences that require alternation
are listed. Some rules, such as lines 125 and 126, are prepared for obscure cases that require acontext of up to three characters to decide the variants. This is inconveniently to be represented
by an automaton, where the status of the tri-grams should be prepared.
Table 6 lists the ﬁnal normalization rules. Sec. 4.4 mentions the upper two rules related to
200B in terms of cleaning the invisible separator to avoid ambiguities. The lower three rules areconcern the Unicode scheme for the recommended encoding. An issue related to the ﬁnal normalization is the use of the backspace key for deletion while inputting. Because the appending andconverting operations are uniform in AKKHARA, a backspace keystroke actually cancels the previous rule application, regardless of the speciﬁc operation. Figure 6 shows examples of cancelingappending and converting operations. To hide such encoding details from users, normalization
substitutions are conducted after all other conversions are performed. This operation exceeds thecapability of an automaton, but is a general rewriting regardless of the previous status.
These 133 rules realize a basic Myanmar input method that can generate well formed Unicodecharacter sequences. Users can add more customized rules. As mentioned, priority is convenient
to group rules, and more sophisticated context dependent conversions can be assigned a priority
from 4. These black-boxes are the core functions of the input method, and all user-added rules
11 Ding et al. (2019) did not include these.

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Fig. 6Vol. 29 No. 4

December 2022(a) Using k and K to input 1000 and 1001, with a backspace keystroke deleting character
1001. (b) Using k and kh to input the two characters and a backspace keystroke to cancel theconversion caused by h. (c) During the input process, 1037 (the lower circle, which is input by
an upper case J) and 103A (the upper arc, which is the end of the string input by x) are notnormalized (by line 133 in Table 6) such that a backspace keystroke deletes 1037.

are applied after the basic operations.

6 Conclusion and Future Work
This paper has described the AKKHARA, a general input method editor that can accommodate
writing systems of medium complexity. A realization of a Romanization-based Myanmar input
method is illustrated as an application case for AKKHARA. AKKHARA is more powerful than a keycharacter mapper, but simpler than the input methods for Chinese and Japanese. Therefore,
AKKHARA is easy to maintain and suitable for realizing customized input methods.
Future engineering developments include: 1) developing AKKHARA for other OSs in addtion
to Windows, 2) improving the rule editing interface, and 3) integrating corpora for prediction
functions. The input methods of more abugida and syllablic systems will be further investigatedand edited using AKKHARA for eﬃcient input and normalized digitization.

References
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Myanmar-English Dictionary(Myanma-anggalip abidan) (12th edition). Ministry of Education, the Republic of the Unionof Myanmar.
Ding, C., Utiyama, M., and Sumita, E. (2018). “Simpliﬁed Abugidas.” In Proceedings of the 56th
Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers),
pp. 491–495, Melbourne, Australia. Association for Computational Linguistics.
Ding, C., Utiyama, M., and Sumita, E. (2019).

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on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pp. 157–162,
Hong Kong, China. Association for Computational Linguistics.
Hopcroft, J. E., Motwani, R., and Ullman, J. D. (2013). Introduction to Automata Theory,
Languages, and Computation (3rd edition). Pearson.
Maddieson, I. (2013a). “Consonant Inventories.” In Dryer, M. S. and Haspelmath, M. (Eds.),
The World Atlas of Language Structures Online. Max Planck Institute for Evolutionary
Anthropology, Leipzig.
Maddieson, I. (2013b). “Vowel Quality Inventories.” In Dryer, M. S. and Haspelmath, M. (Eds.),
The World Atlas of Language Structures Online. Max Planck Institute for Evolutionary
Anthropology, Leipzig.
Riza, H., Purwoadi, M., Uliniansyah, T., Ti, A. A., Aljunied, S. M., Mai, L. C., Thang, V. T.,
Thai, N. P., Sun, R., Chea, V., Khin Mar Soe, Khin Thandar Nwet, Utiyama, M., andDing, C. (2016). “Introduction of the asian language treebank.” In Proc. of O-COCOSDA,
pp. 1–6.

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Appendix

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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32

, ||| 104A ||| 0 ||| 0. ||| 104B ||| 0 ||| 0
0 ||| 1040 ||| 0 ||| 0
1 ||| 1041 ||| 0 ||| 0
2 ||| 1042 ||| 0 ||| 0
3 ||| 1043 ||| 0 ||| 0
4 ||| 1044 ||| 0 ||| 0
5 ||| 1045 ||| 0 ||| 0
6 ||| 1046 ||| 0 ||| 0
7 ||| 1047 ||| 0 ||| 0
8 ||| 1048 ||| 0 ||| 0
9 ||| 1049 ||| 0 ||| 0
A ||| 104F ||| 0 ||| 0
B ||| 1018 ||| 0 ||| 0
C ||| 1006 ||| 0 ||| 0
D ||| 1013 ||| 0 ||| 0
E ||| 1032 ||| 0 ||| 0
F ||| 1039 ||| 0 ||| 0
G ||| 1003 ||| 0 ||| 0
H ||| 101F ||| 0 ||| 0
I ||| 102E ||| 0 ||| 0
J ||| 1037 ||| 0 ||| 0
K ||| 1001 ||| 0 ||| 0
L ||| 1020 ||| 0 ||| 0
M ||| 1036 ||| 0 ||| 0
N ||| 100A ||| 0 ||| 0
O ||| 1029 ||| 0 ||| 0
P ||| 1016 ||| 0 ||| 0
Q ||| 1002 ||| 0 ||| 0
R ||| 101B ||| 0 ||| 0
S ||| 1026 ||| 0 ||| 0
T ||| 1011 ||| 0 ||| 0Table 2

33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
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64

U ||| 1030 ||| 0 ||| 0
V ||| 1021 ||| 0 ||| 0
W ||| 101D ||| 0 ||| 0
X ||| 1004 ||| 0 ||| 0
Y ||| 101A ||| 0 ||| 0
Z ||| 1008 ||| 0 ||| 0
a ||| 1031 102C ||| 0 ||| 0
b ||| 1017 ||| 0 ||| 0
c ||| 1005 ||| 0 ||| 0
d ||| 1012 ||| 0 ||| 0
e ||| 1031 ||| 0 ||| 0
f ||| 103A ||| 0 ||| 0
g ||| 1002 ||| 0 ||| 0
h ||| 103E ||| 0 ||| 0
i ||| 102D ||| 0 ||| 0
j ||| 1038 ||| 0 ||| 0
k ||| 1000 ||| 0 ||| 0
l ||| 101C ||| 0 ||| 0
m ||| 1019 ||| 0 ||| 0
n ||| 1014 ||| 0 ||| 0
o ||| 102D 102F ||| 0 ||| 0
p ||| 1015 ||| 0 ||| 0
q ||| 200B ||| 0 ||| 0
r ||| 103C ||| 0 ||| 0
s ||| 101E ||| 0 ||| 0
t ||| 1010 ||| 0 ||| 0
u ||| 102F ||| 0 ||| 0
v ||| 102C ||| 0 ||| 0
w ||| 103D ||| 0 ||| 0
x ||| 1004 103A ||| 0 ||| 0
y ||| 103B ||| 0 ||| 0
z ||| 1007 ||| 0 ||| 0

Sixty-four rules with a priority of 0.

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65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87

Inputting Writing Systems with Medium Complexity

88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110

1000 103E ||| 1001 ||| 1 ||| 01002 103E ||| 1003 ||| 1 ||| 01005 103E ||| 1006 ||| 1 ||| 01007 103E ||| 1008 ||| 1 ||| 01010 103E ||| 1011 ||| 1 ||| 01012 103E ||| 1013 ||| 1 ||| 01015 103E ||| 1016 ||| 1 ||| 01017 103E ||| 1018 ||| 1 ||| 01004 1002 ||| 100F ||| 1 ||| 0
100A 1002 ||| 1009 ||| 1 ||| 0100F 1002 ||| 104C ||| 1 ||| 01010 1002 ||| 100B ||| 1 ||| 01011 1002 ||| 100C ||| 1 ||| 01012 1002 ||| 100D ||| 1 ||| 01013 1002 ||| 100E ||| 1 ||| 01014 1002 ||| 1004 ||| 1 ||| 01019 1002 ||| 1036 ||| 1 ||| 0
101B 1002 ||| 104D ||| 1 ||| 0
101C 1002 ||| 1020 ||| 1 ||| 0
101E 1002 ||| 103F ||| 1 ||| 01020 1002 ||| 104E ||| 1 ||| 01027 1002 ||| 104F ||| 1 ||| 01029 1002 ||| 102A ||| 1 ||| 0Table 3
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126

102C 1002 ||| 102B ||| 1 ||| 0
102D 1002 ||| 1023 ||| 1 ||| 0102E 1002 ||| 1024 ||| 1 ||| 0102F 1002 ||| 1025 ||| 1 ||| 01030 1002 ||| 1026 ||| 1 ||| 01031 1002 ||| 1027 ||| 1 ||| 01014 103B ||| 100A ||| 1 ||| 0
101E 103C ||| 1029 ||| 1 ||| 0
102D 102D ||| 102E ||| 1 ||| 0102E 102D ||| 102D ||| 1 ||| 0102F 102F ||| 1030 ||| 1 ||| 01030 102F ||| 102F ||| 1 ||| 01031 1031 ||| 1032 ||| 1 ||| 01032 1031 ||| 1031 ||| 1 ||| 01038 1038 ||| 1037 ||| 1 ||| 01037 1038 ||| 1038 ||| 1 ||| 0103A 103A ||| 1039 ||| 1 ||| 01039 103A ||| 103A ||| 1 ||| 0102C 102C ||| 1021 ||| 1 ||| 0
103B 103B ||| 101A ||| 1 ||| 0103C 103C ||| 101B ||| 1 ||| 0
103D 103D ||| 101D ||| 1 ||| 0103E 103E ||| 101F ||| 1 ||| 0

Forty-six rules with a priority of 1.

1001 102C ||| 1001 102B ||| 2 ||| 11002 102C ||| 1002 102B ||| 2 ||| 11004 102C ||| 1004 102B ||| 2 ||| 11012 102C ||| 1012 102B ||| 2 ||| 11015 102C ||| 1015 102B ||| 2 ||| 1
101D 102C ||| 101D 102B ||| 2 ||| 11001 1031 102C ||| 1001 1031 102B ||| 2 ||| 11002 1031 102C ||| 1002 1031 102B ||| 2 ||| 11004 1031 102C ||| 1004 1031 102B ||| 2 ||| 11012 1031 102C ||| 1012 1031 102B ||| 2 ||| 11015 1031 102C ||| 1015 1031 102B ||| 2 ||| 1
101D 1031 102C ||| 101D 1031 102B ||| 2 ||| 11002 103D 102C ||| 1002 103D 102B ||| 2 ||| 11012 103D 102C ||| 1012 103D 102B ||| 2 ||| 11002 103D 1031 102C ||| 1002 103D 1031 102B ||| 2 ||| 11012 103D 1031 102C ||| 1012 103D 1031 102B ||| 2 ||| 1
Table 4Sixteen rules with a priority of 2.

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1015 1039 1015 102B ||| 1015 1039 1015 102C ||| 3 ||| 11015 1039 1015 1031 102B ||| 1015 1039 1015 1031 102C ||| 3 ||| 1
Table 5
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130
131
132
133

Two rules with a priority of 3.

200B 103E ||| 103E ||| -1 ||| 0
200B 1002 ||| 1002 ||| -1 ||| 01025 102E ||| 1026 ||| -1 ||| 01029 1031 102C 103A ||| 102A ||| -1 ||| 0103A 1037 ||| 1037 103A ||| -1 ||| 0Table 6

Five rules with a priority of −1.

Chenchen Ding: received an M.E. degree in computer science from the University of Tsukuba, Japan, in 2012, and a Ph.D. degree in engineering from the
University of Tsukuba, Japan, in 2015. He is currently a senior researcher with
the National Institute of Information and Communications Technology, Japan.
His research interests include computational linguistics and natural language
processing.
Masao Utiyama: received a B.S. in Computer Science from the University ofTsukuba, Japan, in 1992, an M.S. in Computer Science from the Universityof Tsukuba, Japan, in 1994, a Ph.D. in Engineering from the University ofTsukuba, Japan, in 1997. From 1997 to 1999, he was a Research Associate at
Shinshu University, Japan. He has been a member of the National Instituteof Information and Communications Technology, Japan, since 1999, and iscurrently an Executive Researcher at NICT.
Eiichiro Sumita: received his Ph.D. in Engineering from Kyoto University in
1999, and a master’s and bachelor’s in Computer Science from the University
of Electro-Communications in 1982 and 1980, respectively. He is now in the
National Institute of Information and Communication Technology (NICT), itsfellow and the associate director-general of Advanced Speech Translation Research and Development Promotion Center (ASTREC). His research interests
cover machine translation and e-learning. He is a co-recipient of the Maejima
Hisoka Prize in 2013, the Commendation for Science and Technology by the
Minister of Education, Culture, Sports, Science and Technology, Prizes for
Science and Technology in 2010, and the AAMT Nagao Award in 2007.

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Inputting Writing Systems with Medium Complexity(Received April 25, 2022)(Revised July 22, 2022)(Accepted September 1, 2022)

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