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 = 'াটোবাকো'
result=bnorm(word)
print(f"Non-norm:{word}; Norm:{result['normalized']}")
print("--------------------------------------------------")
pprint(result)

output

Non-norm:াটোবাকো; Norm:টোবাকো
--------------------------------------------------
{'given': 'াটোবাকো',
 'normalized': 'টোবাকো',
 'ops': [{'after': 'টোবাকো',
          'before': 'াটোবাকো',
          '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]
                                                    '৺':Isshar 
                                                    '৻':Ganda
                                                    'ঀ':Anji (not '৭')
                                                    'ঌ':li
                                                    'ৡ':dirgho li
                                                    'ঽ':Avagraha
                                                    'ৠ':Vocalic Rr (not 'ঋ')
                                                    '৲':rupi
                                                    '৴':currency numerator 1
                                                    '৵':currency numerator 2
                                                    '৶':currency numerator 3
                                                    '৷':currency numerator 4
                                                    '৸':currency numerator one less than the denominator
                                                    '৹':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={'ঀ':'৭',
                                                                'ঌ':'৯',
                                                                'ৡ':'৯',
                                                                '৵':'৯',
                                                                '৻':'ৎ',
                                                                'ৠ':'ঋ',
                                                                'ঽ':'ই'}
                                            
                                                    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         =   ['অ', 'আ', 'ই', 'ঈ', 'উ', 'ঊ', 'ঋ', 'এ', 'ঐ', 'ও', 'ঔ']
                                                        consonants     =   ['ক', 'খ', 'গ', 'ঘ', 'ঙ', 'চ', 'ছ','জ', 'ঝ', 'ঞ', 
                                                                            'ট', 'ঠ', 'ড', 'ঢ', 'ণ', 'ত', 'থ', 'দ', 'ধ', 'ন', 
                                                                            'প', 'ফ', 'ব', 'ভ', 'ম', 'য', 'র', 'ল', 'শ', 'ষ', 
                                                                            'স', 'হ','ড়', 'ঢ়', 'য়','ৎ']    
                                                        numbers        =    ['০', '১', '২', '৩', '৪', '৫', '৬', '৭', '৮', '৯']
                                                        vowel_diacritics       =   ['া', 'ি', 'ী', 'ু', 'ূ', 'ৃ', 'ে', 'ৈ', 'ো', 'ৌ']
                                                        consonant_diacritics   =   ['ঁ', 'ং', 'ঃ']
    
                                                        > for example you may want to map 'ঽ':Avagraha as 'হ' based on visual similiarity 
                                                            (default:'ই')

                ** 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:{'ঀ':'ক'}
                        : all legacy symbols will be considered valid unicodes. Only 'ঀ' will be changed to 'ক' , 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:{'ঽ':'ই','ৠ':'ঋ'}
                        : 'ঽ' will be changed to 'ই' and 'ৠ' will be changed to 'ঋ'. All other legacy symbols will be removed
'''

my_legacy_maps={'ঌ':'ই',
                'ৡ':'ই',
                '৵':'ই',
                'ৠ':'ই',
                'ঽ':'ই'}
text="৺,৻,ঀ,ঌ,ৡ,ঽ,ৠ,৲,৴,৵,৶,৷,৸,৹"
# 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:   ৺,৻,ঀ,ঌ,ৡ,ঽ,ৠ,৲,৴,৵,৶,৷,৸,৹
case-2 normalized text:   ৺,৻,ঀ,ই,ই,ই,ই,৲,৴,ই,৶,৷,৸,৹
case-2 default normalized text:   ৺,৻,ঀ,ঌ,ৡ,ঽ,ৠ,৲,৴,৵,৶,৷,৸,৹
case-3 normalized text:   ,,,,,,,,,,,,,
case-4 normalized text:   ,,,ই,ই,ই,ই,,,ই,,,,
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)'আরো'==(b)'আরো' ->  False 
    (a) breaks as:['আ', 'র', 'ে', 'া']
    (b) breaks as:['আ', 'র', 'ো']
# Example-2:
(a)পৌঁছে==(b)পৌঁছে ->  False
    (a) breaks as:['প', 'ে', 'ৗ', 'ঁ', 'ছ', 'ে']
    (b) breaks as:['প', 'ৌ', 'ঁ', 'ছ', 'ে']
# Example-3:
(a)সংস্কৄতি==(b)সংস্কৃতি ->  False
    (a) breaks as:['স', 'ং', 'স', '্', 'ক', 'ৄ', 'ত', 'ি']
    (b) breaks as:['স', 'ং', 'স', '্', 'ক', 'ৃ', 'ত', 'ি']

Nukta Normalization:

Example-1:
(a)কেন্দ্রীয়==(b)কেন্দ্রীয় ->  False
    (a) breaks as:['ক', 'ে', 'ন', '্', 'দ', '্', 'র', 'ী', 'য', '়']
    (b) breaks as:['ক', 'ে', 'ন', '্', 'দ', '্', 'র', 'ী', 'য়']
Example-2:
(a)রযে়ছে==(b)রয়েছে ->  False
    (a) breaks as:['র', 'য', 'ে', '়', 'ছ', 'ে']
    (b) breaks as:['র', 'য়', 'ে', 'ছ', 'ে']
Example-3: 
(a)জ়ন্য==(b)জন্য ->  False
    (a) breaks as:['জ', '়', 'ন', '্', 'য']
    (b) breaks as:['জ', 'ন', '্', 'য']

Invalid hosonto

# Example-1:
(a)দুই্টি==(b)দুইটি-->False
    (a) breaks as ['দ', 'ু', 'ই', '্', 'ট', 'ি']
    (b) breaks as ['দ', 'ু', 'ই', 'ট', 'ি']
# Example-2:
(a)এ্তে==(b)এতে-->False
    (a) breaks as ['এ', '্', 'ত', 'ে']
    (b) breaks as ['এ', 'ত', 'ে']
# Example-3:
(a)নেট্ওয়ার্ক==(b)নেটওয়ার্ক-->False
    (a) breaks as ['ন', 'ে', 'ট', '্', 'ও', 'য়', 'া', 'র', '্', 'ক']
    (b) breaks as ['ন', 'ে', 'ট', 'ও', 'য়', 'া', 'র', '্', 'ক']
# Example-4:
(a)এস্আই==(b)এসআই-->False
    (a) breaks as ['এ', 'স', '্', 'আ', 'ই']
    (b) breaks as ['এ', 'স', 'আ', 'ই']
# Example-5: 
(a)'চু্ক্তি'==(b)'চুক্তি' ->  False 
    (a) breaks as:['চ', 'ু', '্', 'ক', '্', 'ত', 'ি']
    (b) breaks as:['চ', 'ু','ক', '্', 'ত', 'ি']
# Example-6:
(a)'যু্ক্ত'==(b)'যুক্ত' ->   False
    (a) breaks as:['য', 'ু', '্', 'ক', '্', 'ত']
    (b) breaks as:['য', 'ু', 'ক', '্', 'ত']
# Example-7:
(a)'কিছু্ই'==(b)'কিছুই' ->   False
    (a) breaks as:['ক', 'ি', 'ছ', 'ু', '্', 'ই']
    (b) breaks as:['ক', 'ি', 'ছ', 'ু','ই']

To+hosonto:

# Example-1:
(a)বুত্পত্তি==(b)বুৎপত্তি-->False
    (a) breaks as ['ব', 'ু', 'ত', '্', 'প', 'ত', '্', 'ত', 'ি']
    (b) breaks as ['ব', 'ু', 'ৎ', 'প', 'ত', '্', 'ত', 'ি']
# Example-2:
(a)উত্স==(b)উৎস-->False
    (a) breaks as ['উ', 'ত', '্', 'স']
    (b) breaks as ['উ', 'ৎ', 'স']

Unwanted doubles(consecutive doubles):

# Example-1: 
(a)'যুুদ্ধ'==(b)'যুদ্ধ' ->  False 
    (a) breaks as:['য', 'ু', 'ু', 'দ', '্', 'ধ']
    (b) breaks as:['য', 'ু', 'দ', '্', 'ধ']
# Example-2:
(a)'দুুই'==(b)'দুই' ->   False
    (a) breaks as:['দ', 'ু', 'ু', 'ই']
    (b) breaks as:['দ', 'ু', 'ই']
# Example-3:
(a)'প্রকৃৃতির'==(b)'প্রকৃতির' ->   False
    (a) breaks as:['প', '্', 'র', 'ক', 'ৃ', 'ৃ', 'ত', 'ি', 'র']
    (b) breaks as:['প', '্', 'র', 'ক', 'ৃ', 'ত', 'ি', 'র']
# Example-4:
(a)আমাকোা==(b)'আমাকো'->   False
    (a) breaks as:['আ', 'ম', 'া', 'ক', 'ে', 'া', 'া']
    (b) breaks as:['আ', 'ম', 'া', 'ক', 'ো']

Vowwels and modifier followed by vowel diacritics:

# Example-1:
(a)উুলু==(b)উলু-->False
    (a) breaks as ['উ', 'ু', 'ল', 'ু']
    (b) breaks as ['উ', 'ল', 'ু']
# Example-2:
(a)আর্কিওোলজি==(b)আর্কিওলজি-->False
    (a) breaks as ['আ', 'র', '্', 'ক', 'ি', 'ও', 'ো', 'ল', 'জ', 'ি']
    (b) breaks as ['আ', 'র', '্', 'ক', 'ি', 'ও', 'ল', 'জ', 'ি']
# Example-3:
(a)একএে==(b)একত্রে-->False
    (a) breaks as ['এ', 'ক', 'এ', 'ে']
    (b) breaks as ['এ', 'ক', 'ত', '্', 'র', 'ে']

Repeated folas:

# Example-1:
(a)গ্র্রামকে==(b)গ্রামকে-->False
    (a) breaks as ['গ', '্', 'র', '্', 'র', 'া', 'ম', 'ক', 'ে']
    (b) breaks as ['গ', '্', 'র', 'া', 'ম', 'ক', 'ে']

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('িত')["normalized"],'ত')
    self.assertEqual(norm('সং্যুক্তি')["normalized"],"সংযুক্তি")
    # Ending
    self.assertEqual(norm("অজানা্")["normalized"],"অজানা")

    #--------------------------------------------- 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'

from bnunicodenormalizer import IndicNormalizer
norm=IndicNormalizer('devanagari')

initialization

'''
    initialize a normalizer
    args:
        language                        :   language identifier from 'devanagari', 'gujarati', 'odiya', 'tamil', 'panjabi', 'malayalam'
        allow_english                   :   allow english letters numbers and punctuations [default:False]
                
'''

ABOUT US

Authors: Bengali.AI in association with OCR Team , APSIS Solutions Limited
Cite Bengali.AI multipurpose grapheme dataset paper

@inproceedings{alam2021large,
  title={A large multi-target dataset of common bengali handwritten graphemes},
  author={Alam, Samiul and Reasat, Tahsin and Sushmit, Asif Shahriyar and Siddique, Sadi Mohammad and Rahman, Fuad and Hasan, Mahady and Humayun, Ahmed Imtiaz},
  booktitle={International Conference on Document Analysis and Recognition},
  pages={383--398},
  year={2021},
  organization={Springer}
}

quaziadib/bnUnicodeNormalizer

bnUnicodeNormalizer

install

useage

Initialization: Bangla Normalizer

Operations

Normalization Problem Examples

IMPORTANT NOTE

unit testing

Issue Reporting

Indic Base Normalizer

ABOUT US