Bangla Unicode Normalization Toolkit
Bangla Unicode Normalization for word normalization
pip install bnunicodenormalizer
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 textnormalized = 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 textoperation: the name of the operation / problem in given textbefore : what the text looked like before the specific operationafter : what the text looks like after the specific operationallow 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
'''
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: ,,,,,,,,,,,,,
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}
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
In all examples (a) is the non-normalized form and (b) is the normalized form
# 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', 'ি']
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']
# 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']
# 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']
# 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', 'ো']
# 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', 'ে']
# Example-1:
(a)গ্র্রামকে==(b)গ্রামকে-->False
(a) breaks as ['গ', '্', 'র', '্', 'র', 'া', 'ম', 'ক', 'ে']
(b) breaks as ['গ', '্', 'র', 'া', 'ম', 'ক', 'ে']
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
testspython3 -m unittest test_normalizer.pyfor reporting an issue please provide the specific information
# 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-
from bnunicodenormalizer import IndicNormalizer
norm=IndicNormalizer('devanagari')
'''
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]
'''
@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}
}