Sign Language Translator Using Artificial Intelligence by IRJET Journal

Sign Language Translator Using Artificial Intelligence

Prof. M. D. Choudhari, Chinmay Pathak, Gunmay Kharabe, Rushikesh Bhise, Shishir Mane Department of Artificial Intelligence & Data Science

K. D.

K. College of Engineering Nagpur, India

Department of Artificial Intelligence & Data Science K. D. K. College of Engineering Nagpur, India

Abstract- A significant communication divide separates users of sign language from those who do not, often leading to the social and professional isolation of the deaf and hard-of-hearing community. This gap is widened by a shortage of real-time, portable, and affordable translation technologies. This project introduces a system engineered to overcome these barriers by translating sign language into text or speech, and vice versa, in real time. The primary aim is to facilitate seamless communication between signers and non-signers. By doing so, the project anticipates fostering greater inclusivity, enhancing independence for deaf individuals, and enabling their full participation in all social and professional environments.

Keywords sign language recognition, computer vision, deep learning, human-computer interaction, continuous SLR, transformers, accessibility technology Introduction

I. INTRODUCTION

Communicationisafundamentalpillarofhumansociety.Forthedeafandhard-of-hearingcommunity,signlanguage a visual language using hand gestures, facial expressions, and body movements is a primary means of communication. However,asignificantcommunicationgapexistsbetweensignlanguageusersandthosewhodonotknowsignlanguage. This barrier can lead to social and professional exclusion, limiting opportunities in education, workplaces, and daily life The reliance on human interpreters is not always feasible or accessible, highlighting an urgent need for an affordable, portable, and accurate technological solution. AI-powered Sign Language Recognition (SLR) systems aim to address this need directly. A Live Sign Language Translator captures gestures in real-time using a standard camera, employing computervision,machinelearning,andnaturallanguageprocessingtorecognizeandtranslatethem.Thegoalistocreate aseamless,two-waycommunicationtoolthatconvertssignlanguageintotextorspeech,andcanalsoconvertspeechinto sign language representations. Such a system promotes greater accessibility and independence for its users. Beyond accessibility,thisprojecthighlightshowAIcanbeappliedforsocialgood,promotinginclusivityandequalparticipationin society. It can be used in schools to support deaf students, in hospitals to assist communication between doctors and patients, and in workplaces to create inclusive environments. Moreover, as wearable and mobile devices evolve, such translators can become portable and user-friendly, making them widely accessible. By eliminating communication barriers, it empowers individuals with hearing impairments to interact freely, gain equal opportunities, and live more independently. This project demonstrates the true potential of AI when applied to human needs, combining technology withcompassionforabettersociety.Thispaperpresentsacomprehensivereviewofthestate-of-the-artinAI-basedSLR Weprovideastructuredoverviewofthetechnologicalevolution,methodologies,andpersistentchallengesinthefield.The key contributions of this review are: 1.A detailed survey of modern deep learning architectures for SLR. 2.An analysis of the role of datasets and the critical bottlenecks related to data scarcity. 3.A thorough discussion of the open challenges defined by the core problem statement. 4.An outlook on future research directions required to build a user-friendly and effectivesystemwithlowlatencythatrequiresnospecialhardwarelikeglovesorsensor.

II. PROBLEM STATEMENT AND OBJECTIVES

Thecentral problemisthelack ofimmediateandaccessiblecommunication betweensignersandnon-signers. Thisleads toseveralinterconnectedissues:

A. Social and Professional Exclusion: Communicationbarriersareaprimarycauseofexclusionforthedeafcommunity.

B. Interpreter Dependency: The reliance on human interpreters is a significant bottleneck, as they are not always availableoraffordable.

C. Technological Gaps: Existingtranslationtoolsareoftenexpensive,notportable,orlackreal-timeaccuracy

D. Linguistic Barriers: Inconsistenciesacrossregionalsignlanguagescanleadtomisscommunicationandlearningsign languagepresentstimeandresourceconstraintsfornon-signers.

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Toaddressthis,theprimaryobjectiveistodevelopareal-timesignlanguagetranslationsystemusingAI.Thekeygoalsfor suchasystemareto:

1) Accuratelyrecognizehandgesturesandfacialexpressions.

2) Translaterecognizedsignsintotextandspeechoutput.

3) Achievehighaccuracyandlowlatencyforsmooth,instantconversation.

4) Ensurethesystemisuser-friendlyandworkswithstandardcameraswithoutextrahardware

III. BACKGROUND: THE LINGUISTICS OF SIGN LANGUAGE

Beforedelvingintothetechnicalmethodologies,itisessentialtounderstandthelinguisticpropertiesofsignlanguage, asthesepropertiesdefinethecorechallengesforanyautomatedrecognitionsystem.Unlikespokenlanguages,whichare sequentialandauditory,signlanguagesarevisual-spatialandcanconveyinformationinparallel.

IV. LITERATURE REVIEW

Ref No.

Author(s) & Year

1) Fang, Co, and Zhang (2018)

Title/ Focus

Deep ASL – Infrared-Based Sentence-LevelTranslation

Key Contribution/ Relevance

DeepASL,asystemusinginfrared sensing plus a hierarchical bidirectional RNN and CTC framework, achieving ~945% accuracy on sentence- level American Sign Language (ASL) translation pioneering nonintrusive,ubiquitoustranslation.

2) Shahin&Ismail(2024) TransformerModelsinSign Language Machine Translation(SLMT) surveyed the evolution from rulebased methods to deep-learning transformerarchitecturesforSLMT, offering a taxonomy and noting transformers outperform previous models in tasks like gloss-to-text translation.

3) Tanetal(2024) Comprehensive Deep Learning Study of Sign LanguageProcessing landscape recognition,translation, generation highlighting limitations of gloss-based approaches,datasetinconsistencies, and evaluation challenges, while emphasizing the emerging role of LargeLanguageModels(LLMs)

4) Abroadreview(2024) Machine Learning with Gesture, Face, and Lip Detection sign language interpretation systemsfromML,computervision, and animation perspectives includinggesture,facialexpression, and lip reading and discussed real-worlddeployedmobileapps

5) Kimetal(2024) Gloss-Free Translation via MultimodalLLMs novel gloss-free SLT framework using large multimodal language models (MLLMs) to describe sign components, aligning them with spokensentences achievingstateof-the-art performance on benchmarkslikePHOENIX14T.

6) Kapiletal(2025) Real-Time Multimodal a real-time multimodal translator combining speech recognition, Media Pipe hand tracking, and

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TranslatorPrototype Google Translate API demonstrating practical bridge between spoken, written, and signedformats.

7) Leppetal.(2025)

8) Kouremenos & Ntalianis(2025)

Community-Centered Sign Language Machine Translation the lack of meaningful user (especially Deaf community) involvement in SLMT R&D, urging more inclusive and representative developmentpractices.

GLaM-Sign: Multimodal GreekAccessibilityDataset

9) ElvinLalsiembulHmar, Bornali Gogoi, Nelson R. Varte (IJERT, Aug 2025)

Sign Language Recognition (SLR); static, continuous, translation tasks, plus architecturesetc.

GLaM-Sign, a multimodal dataset combiningaudio,video,textual,and GreekSignLanguagetranslationsto aid real-time translation with applications in tourism, education, andhealthcare.

highlights attention models, glossfree systems, neuromorphic hardware;identifieschallengeslike signer variability, linguistic complexity; suggests future directions like edge-optimized architectures, inclusive datasets, explainableAI

10) Hanna M. Dostal, JessicaA.Scott,Marissa D. Chappell, Christopher Black (Behavioural Sciences, Aug2025)

11) RefiaDaya, Santiago BerrezuetaGuzman, Stefan Wanger(2025)

12) Hanaa ZainEldin, SamahA.Gamel,Fatma M. Talaat, Mansourah Aljohani, Nadiah A. Baghdadi,AmerMalki, Mahmoud Badawy, Mostafa A. Elhosseini (2025)

13) Jalindar Nivrutti Ekatpure, Divya Bhagvat, Shaikh Aman Hashim,SaniyaRashid, Siddhesh Bhanudas Thombare(2025)

14) The Review of Socionetwork Strategies (2025)

A Scoping Review of LiteracyInterventionsUsing Signed Languages for School-AgeDeafStudents

Virtual Reality in Sign Language Education: Opportunities, Challenges, andtheRoadAhead

Found that integrating signed language into literacy instruction improves access and outcomes; identified gaps (e.g. fewer studies with large samples, underrepresentation in certain regions; variablemethodologies).

gesture recognition + real-time feedback,interactiveenvironments, gamification, personalization, inclusivity; constraints: hardware, accuracy, inclusivity of design; suggests actionable recommendations.

A Survey of Advancements inReal-TimeSignLanguage Translators: Integration withIoTTechnology

ASurveyonSignLanguage Translation Systems: BridgingGestures,Text,and Audio for Enhanced Communication

A Critical Study of Recent Deep Learning-Based Continuous Sign Language Recognition(Review)

LooksatAI/ML/DLapproachesin thesesystems;discusseschallenges like latency, device constraints, varied sign language vocabularies, real-worlddeployment.

Categorizes existing systems; discusseschallengesinrecognition accuracy, usability, accessibility; explores approaches and suggests futurelinestoimprovetranslation quality.

Emphasizes issues like sign boundary detection, temporal segmentation, the need for more realistic datasets, better modelling of non-manual cues (facial, body posture)etc.

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V. METHODOLOGY

1) System Work flow :

As illustrated in the general architecture, a typical SLR system operates as follows:

a) Gesture Capture:Astandardwebcamormobilecameracapturesalivevideostreamoftheuser'sgestures.

b)Preprocessing:Individual framesare extracted fromthe video. Techniquesareappliedtoreduce background noise andnormalizetheimagesforconsistentanalysis.

c) Gesture Recognition:AnAI/MLmodelprocessesthepreprocessedframes.Thismodel,oftenaCNNoraspecialized frameworklikeMediaPipeHandTracking,detectskeyhandlandmarksandclassifiesthegestureasaspecificsign.

d) Translation: Therecognizedsignorsequenceofsignsismappedtoitscorrespondingwordorphrase.

e) Output: Thetranslatedtextisdisplayedon-screenandsimultaneouslyconvertedintoaudiblespeech.

2) Machine Learning Models:

Thecoreoftherecognitionenginecanbeimplementedusingseveralapproaches,rangingfromsimpletocomplex:

a) Rule-Based Mapping: Thismethodinvolvesadirectmappingofarecognizedwordorlettertoapre-storedimage orGIFofthecorrespondingsign.Whilesimpletoimplement,thisapproachisnotscalableforalargevocabulary.

b) CNN for Isolated Signs: For recognizing individual alphabet signs (A-Z) or static gestures, a Convolutional Neural Network(CNN)ishighlyeffective.TheCNNistrainedtoclassifyimagesofhandshapesintodistinctcategories

c) CNN + RNN for Sequences:Torecognizeshortsignsequencesordynamicgestures,ahybridmodelisoftenused.A CNNextractsspatialfeaturesfromeachframe,andaRecurrentNeuralNetwork(RNN),suchasanLSTMorGRU,processes thesequenceofthesefeaturestounderstandthetemporalpattern.

d) Transformer-Based Models: For the highly complex task of continuous sign language translation, Transformer modelshavebecomethestate-of-the-art.AsnotedbyHadfield&Bowden,transformersshowstronggains,althoughthey stillfacechallengeswithnon-manualfeaturesandsignergeneralization.

VII. COMPONENTS OF SIGN LANGUAGE

Asigniscomposedofseveralfundamentalparameters,oftenreferredtoasphonemesinspokenlanguage.Theprimary componentsare:

A. Hand shape: Thespecificconfigurationofthefingersandthumb.

B. Orientation:Thedirectionthepalmisfacing

C. Location:Theplaceonthebodyorinthesigningspacewherethesignisproduced

D. Movement:Thetrajectoryandactionofthehands.

E. Non-Manual Features (NMFs): Thesearecrucialforconveyinggrammaticalinformation,syntax,andemotion.They include:

1) Facial Expressions: Eyebrowmovements,forinstance,candistinguishastatementfromaquestion.

2) Mouth Morphemes: Specificmouthshapescanmodifythemeaningofamanualsign.

3) Body Posture and Head Tilt:Thesecanindicatetense,subject-objectrelationships,orreportedspeech.

VI. BLOCK DIAGRAM

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The parallel and multi-channel nature of these components makes vision-based SLR exceptionally challenging. A robust systemmust notonlytrack thehands butalsoperceive andinterpretsubtlechangesinthesigner'sfaceandupper body simultaneously.

VIII. SIGN LANGUAGE DATASETS

Here are some notable datasets available for ISL recognition research:

A. INCLUDE Dataset: Developed by researchers at IIIT-Delhi, this is one of the most significant ISL datasets. It features continuous sentences signed by multiple native signers. The dataset is particularly valuable for its focus on daily conversationandincludesannotationsforgloss,part-of-speechtags,andcorrespondingHinditranslations.

B. IIT Madras Indian Sign Language Dataset: Thisisalarge-scaledatasetfocusingonisolatedsigns.Itcontainsthousands ofvideosofcommonISLwordssignedbymultipleindividuals,makingitsuitablefortrainingmodelsforisolatedSLR tasks.

C. BITS Pilani ISL Dataset: This collection focuses on static alphabet signs and numbers. It's often used for building foundationalmodelsthatcanrecognizefingerspelling,whichisacrucialcomponentofsignlanguage.

D. Jadavpur University School of Education Technology (JU-SET) Dataset: This dataset includes a vocabulary of isolated wordscommonlyusedinacademicandclassroomsettings,aimingtobridgecommunicationgapsineducation.

IX. KEY CHALLENGES AND OPEN PROBLEMS

Despite significant progress, several major challenges prevent SLR systems from achieving the fluency required for widespreaduse.

1) Co-articulation and Segmentation: In continuous signing, the boundaries between signs are blurred. Identifying whereonesignendsandanotherbeginsisextremelydifficult.

2) Non-Manual Features (NMFs): Asnotedintheliterature,facialexpressionsandbodyposturearevitalgrammatical components but are often weakly modeled or ignored. Accurately recognizing and integrating these features is a key hurdle.

3) Signer Independence: Models often fail to generalize to new users whose signing style differs from the training data. Thisissueof"signergeneralization"isapersistentweakpoint.

4) Data Scarcity and Diversity: The lack of large, varied, and well-annotated datasets remains the single biggest bottleneck.Existingcorporaareoftenlimitedindomain,numberofsigners,orenvironmentalconditions.

5) Scalability: Expandingthesystem’svocabularytothousandsofsignsisanon-trivialtask.Ascalablearchitectureis neededtoallowfortheadditionofmoresignsovertimewithoutacompleteredesign.

X. FUTURE DIRECTIONS AND OPPORTUNITIES

The future of SLR research lies in addressing these challenges. The work done on this project, including the preparationofa datasetwithalphabetsigns,common phrases, andGIFsofsignactions,servesasa foundation. The next stepsalignwiththebroadergoalsinthefield:

A. Model Training and UI Development: The immediate work involves training a robust machine learning model and developingasimple,interactiveuserinterfacefortwo-waycommunication.

B. Advanced Multi-modal Fusion: Future models must effectively integrate features from the hands, face, and body to capturethefulllinguisticrichnessofsignlanguage.

C. Self-Supervised Learning: To combat data scarcity, self-supervised techniques can be used to learn powerful visual representationsfromlargeamountsofunlabeledvideodata.

D. Personalization: Creating models that can adapt to an individual user's signing style would significantly improve usabilityandaccuracy.

E. Community-Centric Development: It is paramount that development is done in collaboration with the Deaf community toensurethefinalproductisuseful,respectful,andtrulymeetstheirneeds.

Arealsignlanguage,however,hasamuchlargerlexicon,akintoaspokenlanguage.Scalingmodelstohandletensof Thousands of signs is a significant challenge due to the "curse of dimensionality" and the increased risk of confusion betweenvisuallysimilarsigns.

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XI. CONCLUSION

AI-basedSignLanguageRecognitionhasmaderemarkablestrides,movingfromconcepttoincreasinglyviableprototypes. The goal is a portable, cost-effective, and user- friendly system that bridges the communication gap for deaf and mute individuals. By leveraging modern AI models like signer variability, and the complexity of language itself remain. Future workmustfocusoncreatingmorerobustandgeneralizablemodels,expandingdatasets,andensuringthatthetechnology isdeveloped ethicallyandinclusively.Theultimatevisionistofosterseamlesscommunication,therebypromoting equal participationandgreaterindependencefortheDeafcommunityinallaspectsoflife.

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BIOGRAPHIES

CHINMAY R PATHAK

Pursuing B Tech final year in Artificial Intelligence And Data Science at K D K College of Engineering, Nagpur

GUNMAY A KHARABE

Pursuing B Tech final year in Artificial Intelligence And Data Science at K. D. K.College of Engineering, Nagpur

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

RUSHIKESH H BHISE

Pursuing B Tech final year in Artificial Intelligence And Data Science at K D K College of Engineering, Nagpur

SHISHIR K. MANE

Pursuing B. Tech final year in Artificial Intelligence And Data Science at K D K College of Engineering, Nagpur