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In e na ional Jou nal o Ad ance and Applied Resea ch
www.ijaa .co.in
ISSN – 2347-7075
Impac Fac o – 8.141
Pee Re iewed
Bi-Mon hly
Vol. 6 No. 38
Sep embe - Oc obe - 2025
Na u al Language P ocessing o Indian Languages: Challenges, Resou ces,
and Di ec ions
Ms. Vikhe Rupali Sopan ao
Assis an P o esso ,
Women’s College o Home Science and BCA, Loni.
Co esponding Au ho – Ms. Vikhe Rupali Sopan ao
DOI - 10.5281/zenodo.17312811
Abs ac :
India’s linguis ic di e si y p esen s bo h a ich oppo uni y and a signi ican challenge o
Na u al Language P ocessing (NLP). This pape su eys he s a e o NLP o Indian languages,
co e ing linguis ic cha ac e is ics, a ailable co po a and benchma ks, ecen model ad ances
(including Indic-speci ic p e- ained models), ask-speci ic p og ess (machine ansla ion, speech,
NER, sen imen analysis), and ou s anding challenges such as low- esou ce se ings, sc ip di e si y,
and code-mixing. We discuss enginee ing s a egies ha ha e shown p omise— ans e lea ning,
ansli e a ion-awa e aining, and mul ilingual p e aining—and ou line esea ch di ec ions
including benchma k s anda diza ion, inclusi e da a collec ion, and applica ion o la ge language
models. A cu a ed lis o esou ces and a ecommended esea ch oadmap a e p o ided o help
esea che s and p ac i ione s plan u u e wo k.
Keywo ds: Indian languages, Indic NLP, mul ilingual models, low- esou ce languages, da ase s,
benchma ks, MuRIL, IndicBERT
In oduc ion:
Indian languages o m a linguis ically
as and a ied geog aphy mul iple amilies(
Indo- A yan, D a idian, Aus oasia ic, Tibe o-
Bu man), nume ous sc ip s, agglu ina i e and
in lec ional mo phologies, and wide law-
mixing wi h English. Despi e a la ge speake
base, nume ous Indian languages a e unde -
esou ced in NLP e ms — limi ed labeled
da a, inconsis en o hog aphy, and many
s anda dized ma ks. A he same ime, digi al
elinquishmen and go e nmen en e p ise
ha e c ea ed ins iga ion o e ec ing usable
language echnologies in he Indian
en i onmen . This pape syn hesizes ecen
p og ess, egis e s co e co e s, and highligh s
algo i hmic and e alua ion equi emen s.
Linguis ic cha ac e is ics ele an o NLP:
• Sc ip di e si y: A single language can be
w i en in di e en sc ip s o ansli e a ed in o
La in; mul iple languages use dis inc sc ip s
wi h ypog aphic p ope ies ha a ec
okeniza ion and OCR.
• Mo phology: Se e al Indian languages show
ich mo phology and compounding, inc easing
spa si y a he wo d le el and making subwo d
app oaches impo an .
• F ee wo d o de : Many Indian languages
allow ela i ely ee wo d o de which impac s
syn ac ic pa sing and alignmen o MT.
• Code-mixing and ansli e a ion: Real-wo ld
ex , especially on social media, o en con ains
sc ip -swi ching and English code-mixing;
models mus handle mixed-sc ip inpu s
obus ly.
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Ms. Vikhe Rupali Sopan ao
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• Dialec al a ia ion: Regional dialec s and
domain-speci ic egis e s (e.g., legal,
ag icul u al) c ea e domain-shi challenges.
Resou ces and Co po a:
Recen communi y e o s ha e
d ama ically imp o ed he esou ce landscape
o Indian languages. Key esou ces include:
• Indic co po a (AI4Bha a / IndicNLP): La ge
monolingual co po a co e ing mul iple majo
Indian languages collec ed om web c awls,
news, and digi al a chi es. These co po a
suppo p e aining and downs eam asks.
• Samanan a : La ge pa allel co po a o
English–Indic language pai s, use ul o
machine ansla ion and c oss-lingual ans e .
• OSCAR and CommonC awl de i a i es:
Noisy bu massi e sou ces o ex o
p e aining.
• Task-speci ic da ase s: NER, POS, QA,
sen imen da ase s p oduced by academic
g oups, sha ed asks, and indus y labs.
• Benchma ks: IndicGLUE and o he pan-
Indic benchma ks colla e se e al NLU asks
ac oss languages o enable compa a i e
e alua ion.
Model De elopmen s:
1. P e- ained mul ilingual and Indic-
speci ic models:
• mBERT / XLM-R: Gene al mul ilingual
models ha p o ide s ong baselines bu
unde ep esen many Indian languages in
aining da a.
• IndicBERT: A amily o models ained
speci ically on mul iple Indic languages o
be e cap u e language-speci ic pa e ns.
• MuRIL: A mul ilingual ep esen a ion model
explici ly ained o Indian languages,
augmen ed wi h ansli e a ed and ansla ed
pai s o help c oss-sc ip and c oss-lingual
pe o mance.
These models demons a e ha
a ge ed p e aining on in-language ex and
ansli e a ion-awa e s a egies signi ican ly
imp o e downs eam pe o mance compa ed
o gene ic mul ilingual models.
2. Task-speci ic me hods:
• T ansli e a ion-awa e okeniza ion:
In eg a ing ansli e a ion pipelines o join
modeling o sc ip a ian s helps educe noise
om La in-sc ip ansli e a ions.
• Mo phology-awa e app oaches: Mo pheme
segmen a ion o subwo d egula iza ion
educes spa si y o mo phologically ich
languages.
• Da a augmen a ion and syn he ic pa allel
da a: Back ansla ion, syn he ic ansli e a ion,
and ansla ion-based da a augmen a ion boos
MT and classi ica ion pe o mance in low-
esou ce scena ios.
Majo NLP Tasks: P og ess & Challenges:
1. Machine T ansla ion (MT):
Neu al MT sys ems ained on pa allel
co po a (Samanan a , e c.) ha e enabled
usable ansla ion o many language pai s,
especially when combined wi h ans e om
high- esou ce languages and back ansla ion.
Challenges include domain misma ch and low-
quali y noisy pa allel da a o se e al
languages.
2. Au oma ic Speech Recogni ion (ASR)
and Tex - o-Speech (TTS):
Speech da ase s and end- o-end
modeling ha e ma u ed o a hand ul o majo
languages, bu many languages s ill lack
sizeable, high-quali y speech co po a. Sc ip
suppo o speech echnologies is complica ed
by o hog aphic no maliza ion issues.
3. Named En i y Recogni ion (NER), POS,
Pa sing:
NER da ase s exis o some majo
languages; howe e , c oss-lingual ans e and
anno a ion s anda ds a y. Language-speci ic
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Ms. Vikhe Rupali Sopan ao
99
POS ags and dependency anno a ion schemes
equi e ha moniza ion o allow mul i-lingual
pa se s.
4. Sen imen and Social Media Analysis:
Code-mixed ex domina es social
media. Models ha explici ly model code-
mixing and ansli e a ion show be e
obus ness. Labeled da ase s emain limi ed
and skewed owa d pa icula dialec s o
domains.
5. Ques ion Answe ing (QA) and Reading
Comp ehension:
QA da ase s ha e been c ea ed o
se e al Indian languages; model pe o mance
imp o es wi h mul ilingual p e aining and
ca e ul da ase ansla ion, bu complex
easoning and cul u ally speci ic knowledge
emain challenging.
E alua ion and benchma ks:
Benchma ks such as IndicGLUE
p o ide uni ied NLU e alua ion ac oss asks
and languages, while newe e o s (e.g.,
Bha a Bench, IndicMMLU a ian s) aim o
b oaden ask co e age and include indus y-
ele an use cases. S anda dized e alua ion is
essen ial o compa e app oaches ai ly and
iden i y whe e esou ces should be alloca ed.
E hical conside a ions, bias, and inclusion:
• Rep esen a ion bias: Mos da ase s
o e ep esen ce ain languages, dialec s, o
o mal egis e s (news), which biases models
owa d hose a ie ies.
• P i acy and consen : Da a collec ion mus
ollow e hical no ms, especially o speech and
use -gene a ed con en .
• Accessibili y: Building inclusi e sys ems
equi es da ase s and e alua ion ha e lec
eal use s, including low-li e acy and non-
s anda d sc ip use s.
Open p oblems and u u e di ec ions:
• Scaling o many low- esou ce languages:
Au oma ed da ase c ea ion, weak supe ision,
and mul ilingual ans e lea ning a e
p omising ou es.
• Be e handling o code-mixing: Join models
ha can p ocess mixed-sc ip and mixed-
language inpu s na i ely.
• LLMs and ins uc ion- ollowing models o
Indic languages: Adap ing la ge language
models and aligning hem o egional
languages and use needs.
• Mul imodal and g ounding: Combining ex ,
speech, and ision o iche applica ions (e.g.,
ag icul u al ad iso ies in local languages).
P ac ical oadmap o esea che s:
1. S a wi h esou ces: Use IndicNLP
co po a and ca alogs o ga he
monolingual and pa allel da a.
2. Choose an app oach: Fo low- esou ce
languages, p io i ize ans e lea ning
om ela ed languages and ansli e a ion
augmen a ion.
3. Benchma k: E alua e on IndicGLUE o
ask-speci ic da ase s; epo language-
wise b eakdowns.
4. E hics & elease: Ensu e documen a ion,
consen ( o speech), and clea license
e ms o da ase /model elease.
Conclusion:
NLP o Indian languages has ma u ed
subs an ially in he pas ew yea s hanks o
communi y e o s and a ge ed modeling
s a egies. Howe e , la ge gaps emain o
many languages and domains. Con inued
ocus on da a collec ion, inclusi e
benchma ks, and language-speci ic modeling
will be equi ed o build obus , equi able
language echnologies o India.
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Ms. Vikhe Rupali Sopan ao
100
Re e ences:
1. D. Kakwani e al., ―Monolingual
Co po a, E alua ion Benchma ks and
P e-…‖ (IndicNLP esou ces).
2. Sim an Khanuja e al., ―MuRIL:
Mul ilingual Rep esen a ions o Indian
Languages‖ (2021).
3. Sim an Khanuja, Sebas ian Rude ,
Pa ha Talukda , ―E alua ing he
Di e si y, Equi y and Inclusion o NLP
Technology: A Case S udy o Indian
Languages‖ (EACL 2023 indings).
4. S Ghosh e al., ―IndicFinNLP: Financial
Na u al Language P ocessing o Indian
Languages‖ (LREC 2024).
5. B.S. Ha ish e al., ―A comp ehensi e
su ey on Indian egional language…‖
(2020).
