bnunicodenormalizer

Bangla Unicode Normalization Toolkit


Keywords
bangla, unicode, text, normalization, indic
License
MIT
Install
pip install bnunicodenormalizer==0.0.8

Documentation

bnUnicodeNormalizer

Bangla Unicode Normalization for word normalization

install

pip install bnunicodenormalizer

useage

initialization and cleaning

# import
from bnunicodenormalizer import Normalizer 
from pprint import pprint
# initialize
bnorm=Normalizer()
# normalize
word = 'āĻžāĻŸā§‹āĻŦāĻžāĻ•ā§‹'
result=bnorm(word)
print(f"Non-norm:{word}; Norm:{result['normalized']}")
print("--------------------------------------------------")
pprint(result)

output

Non-norm:āĻžāĻŸā§‹āĻŦāĻžāĻ•ā§‹; Norm:āĻŸā§‹āĻŦāĻžāĻ•ā§‹
--------------------------------------------------
{'given': 'āĻžāĻŸā§‹āĻŦāĻžāĻ•ā§‹',
 'normalized': 'āĻŸā§‹āĻŦāĻžāĻ•ā§‹',
 'ops': [{'after': 'āĻŸā§‹āĻŦāĻžāĻ•ā§‹',
          'before': 'āĻžāĻŸā§‹āĻŦāĻžāĻ•ā§‹',
          'operation': 'InvalidUnicode'}]}

call to the normalizer returns a dictionary in the following format

  • given = provided text
  • normalized = normalized text (gives None if during the operation length of the text becomes 0)
  • ops = list of operations (dictionary) that were executed in given text to create normalized text
  • each dictionary in ops has:
    • operation: the name of the operation / problem in given text
    • before : what the text looked like before the specific operation
    • after : what the text looks like after the specific operation

allow to use english text

# initialize without english (default)
norm=Normalizer()
print("without english:",norm("ASD123")["normalized"])
# --> returns None
norm=Normalizer(allow_english=True)
print("with english:",norm("ASD123")["normalized"])

output

without english: None
with english: ASD123

Initialization: Bangla Normalizer

'''
    initialize a normalizer
            args:
                allow_english                   :   allow english letters numbers and punctuations [default:False]
                keep_legacy_symbols             :   legacy symbols will be considered as valid unicodes[default:False]
                                                    'ā§ē':Isshar 
                                                    'ā§ģ':Ganda
                                                    'āĻ€':Anji (not 'ā§­')
                                                    'āĻŒ':li
                                                    'ā§Ą':dirgho li
                                                    'āĻŊ':Avagraha
                                                    'ā§ ':Vocalic Rr (not 'āĻ‹')
                                                    'ā§˛':rupi
                                                    'ā§´':currency numerator 1
                                                    'ā§ĩ':currency numerator 2
                                                    'ā§ļ':currency numerator 3
                                                    'ā§ˇ':currency numerator 4
                                                    'ā§¸':currency numerator one less than the denominator
                                                    'ā§š':Currency Denominator Sixteen
                legacy_maps                     :   a dictionay for changing legacy symbols into a more used  unicode 
                                                    a default legacy map is included in the language class as well,
                                                    legacy_maps={'āĻ€':'ā§­',
                                                                'āĻŒ':'ā§¯',
                                                                'ā§Ą':'ā§¯',
                                                                'ā§ĩ':'ā§¯',
                                                                'ā§ģ':'ā§Ž',
                                                                'ā§ ':'āĻ‹',
                                                                'āĻŊ':'āĻ‡'}
                                            
                                                    pass-   
                                                    * legacy_maps=None; for keeping the legacy symbols as they are
                                                    * legacy_maps="default"; for using the default legacy map
                                                    * legacy_maps=custom dictionary(type-dict) ; which will map your desired legacy symbol to any of symbol you want
                                                        * the keys in the custiom dicts must belong to any of the legacy symbols
                                                        * the values in the custiom dicts must belong to either vowels,consonants,numbers or diacritics  
                                                        vowels         =   ['āĻ…', 'āĻ†', 'āĻ‡', 'āĻˆ', 'āĻ‰', 'āĻŠ', 'āĻ‹', 'āĻ', 'āĻ', 'āĻ“', 'āĻ”']
                                                        consonants     =   ['āĻ•', 'āĻ–', 'āĻ—', 'āĻ˜', 'āĻ™', 'āĻš', 'āĻ›','āĻœ', 'āĻ', 'āĻž', 
                                                                            'āĻŸ', 'āĻ ', 'āĻĄ', 'āĻĸ', 'āĻŖ', 'āĻ¤', 'āĻĨ', 'āĻĻ', 'āĻ§', 'āĻ¨', 
                                                                            'āĻĒ', 'āĻĢ', 'āĻŦ', 'āĻ­', 'āĻŽ', 'āĻ¯', 'āĻ°', 'āĻ˛', 'āĻļ', 'āĻˇ', 
                                                                            'āĻ¸', 'āĻš','ā§œ', 'ā§', 'ā§Ÿ','ā§Ž']    
                                                        numbers        =    ['ā§Ļ', 'ā§§', 'ā§¨', 'ā§Š', 'ā§Ē', 'ā§Ģ', 'ā§Ŧ', 'ā§­', 'ā§Ž', 'ā§¯']
                                                        vowel_diacritics       =   ['āĻž', 'āĻŋ', 'ā§€', 'ā§', 'ā§‚', 'ā§ƒ', 'ā§‡', 'ā§ˆ', 'ā§‹', 'ā§Œ']
                                                        consonant_diacritics   =   ['āĻ', 'āĻ‚', 'āĻƒ']
    
                                                        > for example you may want to map 'āĻŊ':Avagraha as 'āĻš' based on visual similiarity 
                                                            (default:'āĻ‡')

                ** legacy contions: keep_legacy_symbols and legacy_maps operates as follows 
                    case-1) keep_legacy_symbols=True and legacy_maps=None
                        : all legacy symbols will be considered valid unicodes. None of them will be changed
                    case-2) keep_legacy_symbols=True and legacy_maps=valid dictionary example:{'āĻ€':'āĻ•'}
                        : all legacy symbols will be considered valid unicodes. Only 'āĻ€' will be changed to 'āĻ•' , others will be untouched
                    case-3) keep_legacy_symbols=False and legacy_maps=None
                        : all legacy symbols will be removed
                    case-4) keep_legacy_symbols=False and legacy_maps=valid dictionary example:{'āĻŊ':'āĻ‡','ā§ ':'āĻ‹'}
                        : 'āĻŊ' will be changed to 'āĻ‡' and 'ā§ ' will be changed to 'āĻ‹'. All other legacy symbols will be removed
'''
my_legacy_maps={'āĻŒ':'āĻ‡',
                'ā§Ą':'āĻ‡',
                'ā§ĩ':'āĻ‡',
                'ā§ ':'āĻ‡',
                'āĻŊ':'āĻ‡'}
text="ā§ē,ā§ģ,āĻ€,āĻŒ,ā§Ą,āĻŊ,ā§ ,ā§˛,ā§´,ā§ĩ,ā§ļ,ā§ˇ,ā§¸,ā§š"
# case 1
norm=Normalizer(keep_legacy_symbols=True,legacy_maps=None)
print("case-1 normalized text:  ",norm(text)["normalized"])
# case 2
norm=Normalizer(keep_legacy_symbols=True,legacy_maps=my_legacy_maps)
print("case-2 normalized text:  ",norm(text)["normalized"])
# case 2-defalut
norm=Normalizer(keep_legacy_symbols=True)
print("case-2 default normalized text:  ",norm(text)["normalized"])

# case 3
norm=Normalizer(keep_legacy_symbols=False,legacy_maps=None)
print("case-3 normalized text:  ",norm(text)["normalized"])
# case 4
norm=Normalizer(keep_legacy_symbols=False,legacy_maps=my_legacy_maps)
print("case-4 normalized text:  ",norm(text)["normalized"])
# case 4-defalut
norm=Normalizer(keep_legacy_symbols=False)
print("case-4 default normalized text:  ",norm(text)["normalized"])

output

case-1 normalized text:   ā§ē,ā§ģ,āĻ€,āĻŒ,ā§Ą,āĻŊ,ā§ ,ā§˛,ā§´,ā§ĩ,ā§ļ,ā§ˇ,ā§¸,ā§š
case-2 normalized text:   ā§ē,ā§ģ,āĻ€,āĻ‡,āĻ‡,āĻ‡,āĻ‡,ā§˛,ā§´,āĻ‡,ā§ļ,ā§ˇ,ā§¸,ā§š
case-2 default normalized text:   ā§ē,ā§ģ,āĻ€,āĻŒ,ā§Ą,āĻŊ,ā§ ,ā§˛,ā§´,ā§ĩ,ā§ļ,ā§ˇ,ā§¸,ā§š
case-3 normalized text:   ,,,,,,,,,,,,,
case-4 normalized text:   ,,,āĻ‡,āĻ‡,āĻ‡,āĻ‡,,,āĻ‡,,,,
case-4 default normalized text:   ,,,,,,,,,,,,, 

Operations

  • base operations available for all indic languages:
self.word_level_ops={"LegacySymbols"   :self.mapLegacySymbols,
                    "BrokenDiacritics" :self.fixBrokenDiacritics}

self.decomp_level_ops={"BrokenNukta"             :self.fixBrokenNukta,
                    "InvalidUnicode"             :self.cleanInvalidUnicodes,
                    "InvalidConnector"           :self.cleanInvalidConnector,
                    "FixDiacritics"              :self.cleanDiacritics,
                    "VowelDiacriticAfterVowel"   :self.cleanVowelDiacriticComingAfterVowel}
  • extensions for bangla
self.decomp_level_ops["ToAndHosontoNormalize"]             =       self.normalizeToandHosonto

# invalid folas 
self.decomp_level_ops["NormalizeConjunctsDiacritics"]      =       self.cleanInvalidConjunctDiacritics

# complex root cleanup 
self.decomp_level_ops["ComplexRootNormalization"]          =       self.convertComplexRoots

Normalization Problem Examples

In all examples (a) is the non-normalized form and (b) is the normalized form

  • Broken diacritics:
# Example-1: 
(a)'āĻ†āĻ°ā§‡āĻž'==(b)'āĻ†āĻ°ā§‹' ->  False 
    (a) breaks as:['āĻ†', 'āĻ°', 'ā§‡', 'āĻž']
    (b) breaks as:['āĻ†', 'āĻ°', 'ā§‹']
# Example-2:
(a)āĻĒā§‡ā§—āĻāĻ›ā§‡==(b)āĻĒā§ŒāĻāĻ›ā§‡ ->  False
    (a) breaks as:['āĻĒ', 'ā§‡', 'ā§—', 'āĻ', 'āĻ›', 'ā§‡']
    (b) breaks as:['āĻĒ', 'ā§Œ', 'āĻ', 'āĻ›', 'ā§‡']
# Example-3:
(a)āĻ¸āĻ‚āĻ¸ā§āĻ•ā§„āĻ¤āĻŋ==(b)āĻ¸āĻ‚āĻ¸ā§āĻ•ā§ƒāĻ¤āĻŋ ->  False
    (a) breaks as:['āĻ¸', 'āĻ‚', 'āĻ¸', 'ā§', 'āĻ•', 'ā§„', 'āĻ¤', 'āĻŋ']
    (b) breaks as:['āĻ¸', 'āĻ‚', 'āĻ¸', 'ā§', 'āĻ•', 'ā§ƒ', 'āĻ¤', 'āĻŋ']
  • Nukta Normalization:
Example-1:
(a)āĻ•ā§‡āĻ¨ā§āĻĻā§āĻ°ā§€āĻ¯āĻŧ==(b)āĻ•ā§‡āĻ¨ā§āĻĻā§āĻ°ā§€ā§Ÿ ->  False
    (a) breaks as:['āĻ•', 'ā§‡', 'āĻ¨', 'ā§', 'āĻĻ', 'ā§', 'āĻ°', 'ā§€', 'āĻ¯', 'āĻŧ']
    (b) breaks as:['āĻ•', 'ā§‡', 'āĻ¨', 'ā§', 'āĻĻ', 'ā§', 'āĻ°', 'ā§€', 'ā§Ÿ']
Example-2:
(a)āĻ°āĻ¯ā§‡āĻŧāĻ›ā§‡==(b)āĻ°ā§Ÿā§‡āĻ›ā§‡ ->  False
    (a) breaks as:['āĻ°', 'āĻ¯', 'ā§‡', 'āĻŧ', 'āĻ›', 'ā§‡']
    (b) breaks as:['āĻ°', 'ā§Ÿ', 'ā§‡', 'āĻ›', 'ā§‡']
Example-3: 
(a)āĻœāĻŧāĻ¨ā§āĻ¯==(b)āĻœāĻ¨ā§āĻ¯ ->  False
    (a) breaks as:['āĻœ', 'āĻŧ', 'āĻ¨', 'ā§', 'āĻ¯']
    (b) breaks as:['āĻœ', 'āĻ¨', 'ā§', 'āĻ¯']
  • Invalid hosonto
# Example-1:
(a)āĻĻā§āĻ‡ā§āĻŸāĻŋ==(b)āĻĻā§āĻ‡āĻŸāĻŋ-->False
    (a) breaks as ['āĻĻ', 'ā§', 'āĻ‡', 'ā§', 'āĻŸ', 'āĻŋ']
    (b) breaks as ['āĻĻ', 'ā§', 'āĻ‡', 'āĻŸ', 'āĻŋ']
# Example-2:
(a)āĻā§āĻ¤ā§‡==(b)āĻāĻ¤ā§‡-->False
    (a) breaks as ['āĻ', 'ā§', 'āĻ¤', 'ā§‡']
    (b) breaks as ['āĻ', 'āĻ¤', 'ā§‡']
# Example-3:
(a)āĻ¨ā§‡āĻŸā§āĻ“ā§ŸāĻžāĻ°ā§āĻ•==(b)āĻ¨ā§‡āĻŸāĻ“ā§ŸāĻžāĻ°ā§āĻ•-->False
    (a) breaks as ['āĻ¨', 'ā§‡', 'āĻŸ', 'ā§', 'āĻ“', 'ā§Ÿ', 'āĻž', 'āĻ°', 'ā§', 'āĻ•']
    (b) breaks as ['āĻ¨', 'ā§‡', 'āĻŸ', 'āĻ“', 'ā§Ÿ', 'āĻž', 'āĻ°', 'ā§', 'āĻ•']
# Example-4:
(a)āĻāĻ¸ā§āĻ†āĻ‡==(b)āĻāĻ¸āĻ†āĻ‡-->False
    (a) breaks as ['āĻ', 'āĻ¸', 'ā§', 'āĻ†', 'āĻ‡']
    (b) breaks as ['āĻ', 'āĻ¸', 'āĻ†', 'āĻ‡']
# Example-5: 
(a)'āĻšā§ā§āĻ•ā§āĻ¤āĻŋ'==(b)'āĻšā§āĻ•ā§āĻ¤āĻŋ' ->  False 
    (a) breaks as:['āĻš', 'ā§', 'ā§', 'āĻ•', 'ā§', 'āĻ¤', 'āĻŋ']
    (b) breaks as:['āĻš', 'ā§','āĻ•', 'ā§', 'āĻ¤', 'āĻŋ']
# Example-6:
(a)'āĻ¯ā§ā§āĻ•ā§āĻ¤'==(b)'āĻ¯ā§āĻ•ā§āĻ¤' ->   False
    (a) breaks as:['āĻ¯', 'ā§', 'ā§', 'āĻ•', 'ā§', 'āĻ¤']
    (b) breaks as:['āĻ¯', 'ā§', 'āĻ•', 'ā§', 'āĻ¤']
# Example-7:
(a)'āĻ•āĻŋāĻ›ā§ā§āĻ‡'==(b)'āĻ•āĻŋāĻ›ā§āĻ‡' ->   False
    (a) breaks as:['āĻ•', 'āĻŋ', 'āĻ›', 'ā§', 'ā§', 'āĻ‡']
    (b) breaks as:['āĻ•', 'āĻŋ', 'āĻ›', 'ā§','āĻ‡']
  • To+hosonto:
# Example-1:
(a)āĻŦā§āĻ¤ā§āĻĒāĻ¤ā§āĻ¤āĻŋ==(b)āĻŦā§ā§ŽāĻĒāĻ¤ā§āĻ¤āĻŋ-->False
    (a) breaks as ['āĻŦ', 'ā§', 'āĻ¤', 'ā§', 'āĻĒ', 'āĻ¤', 'ā§', 'āĻ¤', 'āĻŋ']
    (b) breaks as ['āĻŦ', 'ā§', 'ā§Ž', 'āĻĒ', 'āĻ¤', 'ā§', 'āĻ¤', 'āĻŋ']
# Example-2:
(a)āĻ‰āĻ¤ā§āĻ¸==(b)āĻ‰ā§ŽāĻ¸-->False
    (a) breaks as ['āĻ‰', 'āĻ¤', 'ā§', 'āĻ¸']
    (b) breaks as ['āĻ‰', 'ā§Ž', 'āĻ¸']
  • Unwanted doubles(consecutive doubles):
# Example-1: 
(a)'āĻ¯ā§ā§āĻĻā§āĻ§'==(b)'āĻ¯ā§āĻĻā§āĻ§' ->  False 
    (a) breaks as:['āĻ¯', 'ā§', 'ā§', 'āĻĻ', 'ā§', 'āĻ§']
    (b) breaks as:['āĻ¯', 'ā§', 'āĻĻ', 'ā§', 'āĻ§']
# Example-2:
(a)'āĻĻā§ā§āĻ‡'==(b)'āĻĻā§āĻ‡' ->   False
    (a) breaks as:['āĻĻ', 'ā§', 'ā§', 'āĻ‡']
    (b) breaks as:['āĻĻ', 'ā§', 'āĻ‡']
# Example-3:
(a)'āĻĒā§āĻ°āĻ•ā§ƒā§ƒāĻ¤āĻŋāĻ°'==(b)'āĻĒā§āĻ°āĻ•ā§ƒāĻ¤āĻŋāĻ°' ->   False
    (a) breaks as:['āĻĒ', 'ā§', 'āĻ°', 'āĻ•', 'ā§ƒ', 'ā§ƒ', 'āĻ¤', 'āĻŋ', 'āĻ°']
    (b) breaks as:['āĻĒ', 'ā§', 'āĻ°', 'āĻ•', 'ā§ƒ', 'āĻ¤', 'āĻŋ', 'āĻ°']
# Example-4:
(a)āĻ†āĻŽāĻžāĻ•ā§‡āĻžāĻž==(b)'āĻ†āĻŽāĻžāĻ•ā§‹'->   False
    (a) breaks as:['āĻ†', 'āĻŽ', 'āĻž', 'āĻ•', 'ā§‡', 'āĻž', 'āĻž']
    (b) breaks as:['āĻ†', 'āĻŽ', 'āĻž', 'āĻ•', 'ā§‹']
  • Vowwels and modifier followed by vowel diacritics:
# Example-1:
(a)āĻ‰ā§āĻ˛ā§==(b)āĻ‰āĻ˛ā§-->False
    (a) breaks as ['āĻ‰', 'ā§', 'āĻ˛', 'ā§']
    (b) breaks as ['āĻ‰', 'āĻ˛', 'ā§']
# Example-2:
(a)āĻ†āĻ°ā§āĻ•āĻŋāĻ“ā§‹āĻ˛āĻœāĻŋ==(b)āĻ†āĻ°ā§āĻ•āĻŋāĻ“āĻ˛āĻœāĻŋ-->False
    (a) breaks as ['āĻ†', 'āĻ°', 'ā§', 'āĻ•', 'āĻŋ', 'āĻ“', 'ā§‹', 'āĻ˛', 'āĻœ', 'āĻŋ']
    (b) breaks as ['āĻ†', 'āĻ°', 'ā§', 'āĻ•', 'āĻŋ', 'āĻ“', 'āĻ˛', 'āĻœ', 'āĻŋ']
# Example-3:
(a)āĻāĻ•āĻā§‡==(b)āĻāĻ•āĻ¤ā§āĻ°ā§‡-->False
    (a) breaks as ['āĻ', 'āĻ•', 'āĻ', 'ā§‡']
    (b) breaks as ['āĻ', 'āĻ•', 'āĻ¤', 'ā§', 'āĻ°', 'ā§‡']
  • Repeated folas:
# Example-1:
(a)āĻ—ā§āĻ°ā§āĻ°āĻžāĻŽāĻ•ā§‡==(b)āĻ—ā§āĻ°āĻžāĻŽāĻ•ā§‡-->False
    (a) breaks as ['āĻ—', 'ā§', 'āĻ°', 'ā§', 'āĻ°', 'āĻž', 'āĻŽ', 'āĻ•', 'ā§‡']
    (b) breaks as ['āĻ—', 'ā§', 'āĻ°', 'āĻž', 'āĻŽ', 'āĻ•', 'ā§‡']

IMPORTANT NOTE

The normalization is purely based on how bangla text is used in Bangladesh(bn:bd). It does not necesserily cover every variation of textual content available at other regions

unit testing

  • clone the repository
  • change working directory to tests
  • run: python3 -m unittest test_normalizer.py

Issue Reporting

  • for reporting an issue please provide the specific information

    • invalid text
    • expected valid text
    • why is the output expected
    • clone the repository
    • add a test case in tests/test_normalizer.py after line no:91
        # Dummy Non-Bangla,Numbers and Space cases/ Invalid start end cases
        # english
        self.assertEqual(norm('ASD1234')["normalized"],None)
        self.assertEqual(ennorm('ASD1234')["normalized"],'ASD1234')
        # random
        self.assertEqual(norm('āĻŋāĻ¤')["normalized"],'āĻ¤')
        self.assertEqual(norm('āĻ¸āĻ‚ā§āĻ¯ā§āĻ•ā§āĻ¤āĻŋ')["normalized"],"āĻ¸āĻ‚āĻ¯ā§āĻ•ā§āĻ¤āĻŋ")
        # Ending
        self.assertEqual(norm("āĻ…āĻœāĻžāĻ¨āĻžā§")["normalized"],"āĻ…āĻœāĻžāĻ¨āĻž")
    
        #--------------------------------------------- insert your assertions here----------------------------------------
        '''
            ###  case: give a comment about your case
            ## (a) invalid text==(b) valid text <---- an example of your case
            self.assertEqual(norm(invalid text)["normalized"],expected output)
                        or
            self.assertEqual(ennorm(invalid text)["normalized"],expected output) <----- for including english text
            
        '''
        # your case goes here-
            
    • perform the unit testing
    • make sure the unit test fails under true conditions

Indic Base Normalizer

  • to use indic language normalizer for 'devanagari', 'gujarati', 'odiya', 'tamil', 'panjabi', 'malayalam','sylhetinagri'
from bnunicodenormalizer import IndicNormalizer
norm=IndicNormalizer('devanagari')
  • initialization
'''
    initialize a normalizer
    args:
        language                        :   language identifier from 'devanagari', 'gujarati', 'odiya', 'tamil', 'panjabi', 'malayalam','sylhetinagri'
        allow_english                   :   allow english letters numbers and punctuations [default:False]
                
'''        
        

ABOUT US

@inproceedings{ansary-etal-2024-unicode-normalization,
    title = "{U}nicode Normalization and Grapheme Parsing of {I}ndic Languages",
    author = "Ansary, Nazmuddoha  and
      Adib, Quazi Adibur Rahman  and
      Reasat, Tahsin  and
      Sushmit, Asif Shahriyar  and
      Humayun, Ahmed Imtiaz  and
      Mehnaz, Sazia  and
      Fatema, Kanij  and
      Rashid, Mohammad Mamun Or  and
      Sadeque, Farig",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1479",
    pages = "17019--17030",
    abstract = "Writing systems of Indic languages have orthographic syllables, also known as complex graphemes, as unique horizontal units. A prominent feature of these languages is these complex grapheme units that comprise consonants/consonant conjuncts, vowel diacritics, and consonant diacritics, which, together make a unique Language. Unicode-based writing schemes of these languages often disregard this feature of these languages and encode words as linear sequences of Unicode characters using an intricate scheme of connector characters and font interpreters. Due to this way of using a few dozen Unicode glyphs to write thousands of different unique glyphs (complex graphemes), there are serious ambiguities that lead to malformed words. In this paper, we are proposing two libraries: i) a normalizer for normalizing inconsistencies caused by a Unicode-based encoding scheme for Indic languages and ii) a grapheme parser for Abugida text. It deconstructs words into visually distinct orthographic syllables or complex graphemes and their constituents. Our proposed normalizer is a more efficient and effective tool than the previously used IndicNLP normalizer. Moreover, our parser and normalizer are also suitable tools for general Abugida text processing as they performed well in our robust word-based and NLP experiments. We report the pipeline for the scripts of 7 languages in this work and develop the framework for the integration of more scripts.",
}