International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
Sign Language Detection System Using Machine Learning
Mohd Kaif 1 , Sujal Lathre2 , B. Srinivas Rao3 Neetesh Kumar Nema4
1,2,3B.Tech Student Department of Computer Science and Engineering, LCIT Bilaspur, CG, India
4Assistant Professor, Department of Computer Science and Engineering, LCIT Bilaspur, CG, India
Abstract – Sign language serves as a primary mode of communication for the hearing and speech-impaired community. However, the lack of widespread knowledge of sign language presents a significant communication barrier. This research proposes a machine learning-based sign language detection system that translates sign language gestures into text. The system utilizes deep learning techniques, particularly Convolutional Neural Networks (CNNs), for image recognition and classification. The study outlines the dataset, preprocessing steps, model selection, trainingprocess,andevaluationresults.Theproposedsystem demonstratespromisingaccuracy,makingitaviablesolution for real-world applications in accessibility tools and humancomputer interaction. Furthermore, the research aims to bridge the communication gap by developing a scalable and efficient sign language recognition model.
By leveraging machine learning, the system can provide a robust solution for individuals with speech and hearing impairments. The findings suggest that an automated sign language detection system has the potential to revolutionize accessibility,offeringareal-timetranslationmechanismthat enhances communication between sign language users and non-sign language speakers. Additionally, this research exploreshowtechnologicaladvancementscanbeincorporated into assistive technologies to further bridge the accessibility gap.
Key Words: Sign Language Recognition, Gesture Recognition, Deep Learning, Computer Vision, Convolutional Neural Networks (CNN), Speech-to-Text Conversion, Sign Language Dataset
1. INTRODUCTION
Signlanguageisastructuredvisuallanguagethatuseshand gestures, facial expressions, and body movements for communication. It is an essential mode of interaction for individualswithhearingandspeechdisabilities.Despiteits significance, accessibility remains a challenge due to a limitednumberofsignlanguageinterpretersandageneral lackofawarenessamongthehearingpopulation.
Theadventofartificialintelligence(AI)anddeeplearning provides an opportunity to automate sign language recognition, thereby bridging the communication gap. Machine learning-based solutions offer an approach that leveragesvastamountsofdatatoidentifypatternsinsign gesturesaccurately.Thegoalofthisresearchistodevelopan
efficientsignlanguagedetectionsystemusingdeeplearning models, ensuring accurate real-time translation of sign language into text. This study also evaluates the system’s effectivenessandexplorespotentialapplicationsinassistive technologies.
Additionally,thisresearchhighlightsthechallengesfacedby sign language users in various environments, such as educationalinstitutions,workplaces,andsocialsettings.By incorporatingmachinelearningsolutions,thestudyprovides an innovative approach to making communication more inclusive and accessible to the broader community. The study also aims to address the barriers associated with traditionalcommunicationmethodsandproposessolutions to enhance user experience through advanced AI-driven techniques.
2. LITERATURE REVIEW
Severalmachinelearningapproacheshavebeenexploredfor sign language recognition, ranging from traditional image processing techniques to modern deep learning-based solutions. Earlier methods involved handcrafted feature extraction using edge detection, color segmentation, and contour-based analysis. However, these methods often struggledwithvariationsinlighting,backgroundnoise,and handpositioning.
Recentadvancementsin deeplearning,particularlyCNNs, havesignificantlyimprovedimageclassificationtasks.CNNs learn spatial hierarchies of features, making them wellsuitedforrecognizingintricatehandgestures.Researchhas shown that architectures like ResNet, VGGNet, and MobileNet achieve high accuracy in sign language classificationtasks.Additionally,techniquessuchastransfer learning and data augmentation have further enhanced modelgeneralization.Variousresearchershavecontributed significantlytodifferentaspectsofsignlanguagedetection. Belowaresomeofthenotablestudies.
2.1. Hidden Markov Models for Sign Language Recognition – Starner and Pentland (1995)
StarnerandPentlandpioneeredtheapplicationofHidden MarkovModels(HMMs)forAmericanSignLanguage(ASL) recognition.Theirsystemusedcamera-basedhandtracking andstatisticalmodelingtorecognizedynamichandgestures. Althoughtheirmethoddemonstratedhighaccuracy,itwas computationally intensive and struggled with real-time applicationsduetohardwarelimitationsatthetime.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
2.2. Glove-Based Sign Language Recognition – Fang et al. (2004)
Fang and colleagues introduced a sensor-glove-based approach for sign language detection. The system utilized bendsensorsandaccelerometerstotrackhandmovements accurately. While this method achieved high precision, it required users to wear gloves, making it impractical for widespread adoption due to accessibility concerns and devicedependency.
2.3. Handcrafted Feature Extraction for Gesture Recognition – Bowden et al. (2003)
Bowden and his team developed a Principal Component Analysis (PCA)-based technique for recognizing hand gestures in sign language. Their method relied on handcraftedfeatureextraction,includingedgedetectionand contouranalysis.However,theirapproachlackedrobustness whenappliedtocomplexbackgroundsandvaryinglighting conditions,limitingitsreal-worldusability.
2.4. Deep Learning for Sign Language Recognition –Koller et al. (2016)
KollerandcolleaguesproposedahybridCNN-RNNmodelfor recognizing continuous sign language gestures. Their approachcombinedConvolutionalNeuralNetworks(CNNs) forspatialfeatureextractionandRecurrentNeuralNetworks (RNNs) for temporal sequence modeling. Their work significantly improved recognition accuracy but required large-scaleannotateddatasetsforoptimalperformance.
2.5. Human-Computer Interaction in Sign Language Recognition – Kagita et al. (2022)
KagitaandcolleaguesintegratedOpenPoseandTensorFlow todevelopareal-timesignlanguagetranslationsystemfor human-computerinteraction.Theirmodelextractedskeletal keypoints of hands and facial expressions to enhance recognition accuracy. However, the system’s reliance on high-qualitycamerainputlimiteditsusabilityinlow-lightor crowdedenvironments.
ThisstudybuildsuponexistingresearchbyintegratingCNNs withreal-timeimageprocessingtoenhancesignlanguage recognition efficiency. By leveraging state-of-the-art deep learningtechniques,theproposedmodelaimstoovercome the limitations of traditional approaches. The literature review also explores various dataset collection methods, preprocessing techniques, and evaluation metrics used in sign language recognition research. Additionally, a comparative analysis is conducted between different machinelearningmodelstoassesstheireffectivenessinrealworldscenarios.
3. PROBLEM STATEMENT
Communication barriers exist for individuals who rely on sign language, particularly when interacting with people who do not understand it. This creates challenges in accessibility,education,employment,andsocialinclusionfor thedeafandhard-of-hearingcommunities.Traditionalsign languageinterpretationrequireshumaninterpreters,which maynotalwaysbeavailable,andexistingsolutionsbasedon rule-based algorithms lack accuracy and flexibility. Therefore, an intelligent system capable of accurately recognizing and interpreting sign language gestures is needed.
This project aims to develop a Sign Language Detection Systemusingmachinelearningtechniquestorecognizehand gesturesandtranslatethemintotextorspeechinrealtime. Thesystemwillutilizecomputervisionanddeeplearning models to classify different signs and improve communication between sign language users and nonsigners.
4. OBJECTIVE OF STUDY
Theprimaryobjectiveofthisstudyistodevelopanefficient and accurate Sign Language Detection System using machine learning techniques tofacilitatecommunication betweensignlanguageusersandnon-signers.Thesystem willleverage computer vision and deep learning modelsto recognize hand gestures and translate them into text or speechinreal-time.
1. Todesignandimplementa machinelearning-based modelcapableofdetectingandinterpretingsignlanguage gestureswithhighaccuracy.
2. To enhance accessibility for the deaf and hard-ofhearingcommunitybyprovidinganautomatedtranslation systemforsignlanguage.
3. To improve recognition accuracy by using deep learningtechniquessuchasConvolutionalNeuralNetworks (CNNs)andRecurrentNeuralNetworks(RNNs)forgesture classification.
4. To create a real-time system that can process and translate sign language gestures efficiently without significantlatency.
5. To handle variations in hand gestures, orientations, andlightingconditionstoensurerobustnessandusability acrossdifferentenvironments.
6. To support multiple sign languages (e.g., American SignLanguage(ASL),BritishSignLanguage(BSL),Indian SignLanguage(ISL),etc.)forwideradoption.
7. To evaluate the system’s performance using realworld datasets and user feedback, ensuring practical applicabilityandaccuracy.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
5. LIMITATIONS
While the proposed Sign Language Detection System demonstrates promising results in real-time sign recognition, it has certain limitations that need to be addressedforbroaderapplicability.
1. Limited Vocabulary of Signs
Thesystemistrainedonacustomdatasetwithapredefined setofhandgestures.Thisrestrictsitsabilitytorecognizea widerangeofsignlanguagewordsandphrases,makingit less versatile compared to human interpreters or larger deep-learning-based models. Expanding the dataset to include dynamic gestures and complex sentences would improveitsusability.
2. Challenges in Gesture Variability
Signlanguagegesturescanvarysignificantlydependingon factorssuchas:
Handorientationandmovementspeed
Personalvariationsinhandshapeandsize
Differentlightingconditionsandbackgroundnoise
AlthoughMediaPipeensuresrobusthandtracking,real-time variationsinhandgesturesmayaffectrecognitionaccuracy, particularlywhengesturesareexecutedtoofastoratangles notwell-representedinthetrainingdataset.
3. Dependency on Camera Quality and Positioning
The system’s performance is heavily influenced by the camera’sresolution,framerate,andpositioning.Low-quality cameras or incorrect angles may result in poor feature extraction, leading to misclassification or failure to detect certain gestures. Additionally, background clutter and occlusions(suchasoverlappinghands)canaffectdetection accuracy.
4. Lack of Contextual Understanding
Unlike Natural Language Processing (NLP)-based approaches,oursystemfocusesonindividualsigndetection rather than understanding the context or meaning of full sentences. Many sign languages rely on facial expressions andbodymovements,whichourmodelcurrentlydoesnot accountfor.Futureimprovementscouldinvolveintegrating facial expression analysis and NLP techniques to enhance sentence-leveltranslation.
5. Real-Time Performance Trade-offs
Whilethesystemisdesignedtoworkinreal-time,achieving thisrequiresoptimizingforefficiencyovercomplexfeature extraction. This trade-off means that while the model is
lightweightandfast,itmaynotgeneralizeaswellasdeeplearning-based approaches trained on extensive datasets withmillionsoflabeledgestures.
6. Lack of Multilingual Sign Language Support
Different regions use different variations of sign language (e.g., ASL, BSL, ISL). The system is currently trained on a single dataset, making it incompatible with multiple sign languagesunlesstrainedseparatelyforeach.Developinga universal sign recognition model would require a significantlylargerandmorediversedataset.
7. Limited Dataset Size
Sincethesystemisbuiltonacustomdataset,itmaynotbe ascomprehensiveaspubliclyavailablelarge-scaledatasets. Thislimitationmayimpactmodelgeneralization,requiring more data augmentation, transfer learning, or additional trainingonexternaldatasetstoimproveperformance.
6. WORKING PRINCIPLE
6.1. Feature
Extraction
Featureextractioninvolvesidentifyingkeycomponentssuch ashandshape,fingerposition,andmovementtrajectories. DeeplearningfeaturemapsobtainedfromCNNlayersplaya crucial role in extracting these features automatically. Traditional handcrafted feature extraction methods are inefficient compared to deep learning-based feature learning, which allows for hierarchical representation learning.
6.2.
Model Selection
A CNN-based architecture, such as ResNet-50 or MobileNetV2, is employed forsignlanguageclassification. These models are selected based on their computational efficiencyandhighaccuracyinimagerecognitiontasks.The use of pre-trained models via transfer learning further improvesaccuracywithlimitedtrainingdata.Themodelis fine-tuned to recognize hand gestures effectively by adjusting layer weights and optimizing feature representations.
6.3. Training and Optimization
The model is trained using labeled sign language images, with hyperparameter tuning applied for improved performance. The training process involves data augmentation,batchnormalization,anddropouttechniques to prevent overfitting. Optimization techniques such as Adamoptimizerandcross-entropylossfunctionareusedto enhance learning stability and convergence. The training process is conducted in multiple phases, with each phase focusing on different levels of abstraction in gesture recognition.Additionally,real-timeaugmentationisapplied
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
during training to improve robustness against diverse backgroundsandlightingconditions.
6.4. Real Time Processing and Deployment
Oncetrainedandoptimized,themodelisdeployedforrealtimesignlanguagedetectionusingcamerainput.Thesystem processeslivevideostreams,extractsframes,andpredicts the corresponding text output, enabling instant gesture translation. This step ensures that the model is efficient, scalable,andcapableofassistingindividualswithspeechor hearingimpairmentsinreal-worldinteractions.
Thestructuredmethodologycanbevisuallyrepresentedin theflowchartbelow:
Flowchart Diagram for Sign Language Detection System –
1. Start
2. InputVideo/CameraFeed–Capturehand gesturesusingawebcamorvideoinput.
3. Preprocessing
Convertimagetograyscale/RGB.
Resizeandnormalizeimages.
Backgroundnoiseremoval.
4. FeatureExtraction
Identifyhandlandmarksandkeypoints.
Extractspatialandtemporalfeatures fromgestures.
5. SignClassification(MachineLearningModel)
UseCNN,RNN,orTransformer-based modelstoclassifygestures.
6. PredictionOutput
Convertdetectedsigntotextorspeech
7. Display/AudioOutput
Showpredictedword/textonscreen.
Usetext-to-speech(TTS)foraudio output.
8. End
Flowchart Representation: Start
InputVideo/CameraFeed
Preprocessing (Resizing,Normalizing, NoiseReduction,etc.)
FeatureExtraction (HandLandmarks,Keypoints)
MachineLearningModel (CNN/RNN/Transformer)
SignClassification (IdentifyGesture/Word)
ConverttoText/Speech (PredictionOutput)
DisplayonScreen/AudioOutput
Architecture Diagram
Components of the System:
1. Input (Camera/Video Feed)
o Captureshandgestures,movements,and facialexpressions.
2. Preprocessing
o Imageenhancement(grayscale conversion,noisereduction,background removal).
o Hand/fingersegmentation.
3. Feature Extraction
o Keypointdetection(e.g.,usingMediaPipe orOpenPose).
o Handshapeandmovementanalysis.
4. Machine Learning Model
o CNN(ConvolutionalNeuralNetwork)for image-basedrecognition.
o LSTM(LongShort-TermMemory)for sequence-basedrecognition.
Fig -1: Architecture of Sign Language Recognition System
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
5. Prediction & Classification
o Convertsdetectedgesturesintotextor speech.
6. Output Module
o Displaysrecognizedtext/signmeaning.
o Convertstexttospeechfor communication.
7. EXPERIMENTATION AND RESULTS
The sign language detection system was developed and evaluated through a series of experiments to assess its accuracy, efficiency, and robustness. The dataset used for training consisted of thousands of labeled sign language images,includingvariationsinlighting,handorientations, and backgrounds. To ensure model generalization, data augmentation techniques such as rotation, flipping, and brightnessadjustmentswereapplied.
Forthemodelselection,multipledeeplearningarchitectures weretested,includingResNet-50,MobileNetV2,andVGG-16. Themodelsweretrainedusingacategoricalcross-entropy
lossfunctionandoptimizedwiththeAdamoptimizer.The datasetwassplitinto80%fortraining,10%forvalidation, and10%fortesting.Trainingwasconductedfor50epochs, with batch normalization and dropout applied to prevent over-fitting.
Thereal-timeSignLanguageDetectionSystemdevelopedin thisresearchwasevaluatedbasedonmultipleperformance metrics,includingaccuracy,precision,recall,andF1-score. The model successfully classified sign gestures with high accuracy,achievinganaverageF1-scoreof92.5%acrossall detected signs. The use of MediaPipe for hand landmark detectionprovidedanefficientandlightweightalternativeto deeplearning-basedfeatureextraction.OpenCVimproved frame preprocessing, while Scikit-Learn’s classification modelsensuredeffectiverecognitionofhandgestures.
Theevaluationmetricsusedtoassessmodel performance includedaccuracy,precision,recall,andF1-score.Thefinal trained model achieved an accuracy of 94.7% on the test dataset.Theprecision,recall,andF1-scorewerecalculated fordifferentsignclasses,andtheresultsarevisualizedinthe followinggraph:
The performance metrics graph above illustrates the precision,recall,andF1-scoreacrossdifferentsignlanguage gestureclasses.TheF1-score,whichbalancesprecisionand recall, remains consistently high across all sign classes, confirmingthemodel’sreliability.
From the results, the model shows strong classification performance with an average F1-score of approximately 0.93, indicating effective recognition of sign language gestures.However,slightperformancevariationsexistdue to factors like gesture similarity and hand position variations.
8. CONCLUSION & FUTURE SCOPE
Theproposedsignlanguagedetectionsystemsuccessfully translates sign language gestures into text using deep learning techniques, particularly Convolutional Neural Networks(CNNs).Thesystemdemonstrateshighaccuracy inrecognizingvarioussigngestures,makingitapromising assistive tool for individuals with hearing and speech impairments.
Despite these advancements, challenges remain in realworld deployment. Factors such as lighting variations, backgroundnoise,anddiversehandgesturescanaffectthe model’saccuracy.Futureworkshouldfocusonexpanding thedatasettoincludemultiplesignlanguagesandvariations in gesture execution. Additionally, real-time processing optimizationiscrucialformakingthesystemmorepractical. Implementing lightweight models like MobileNet and leveraging edge computing can help reduce latency and improveefficiency.
Animportantenhancementforfutureresearchiscontextaware translation through Natural Language Processing (NLP).Signlanguagesrelyonsequencesofgesturesrather thanisolatedsigns,andintegratingNLPcanhelpgenerate moremeaningfultranslations.Furthermore,incorporating sign-to-speech conversion can improve accessibility by enablingspokenlanguageoutput.
In conclusion, this study presents a robust foundation for signlanguagedetectionusingmachinelearning.Withfurther improvementsindatasetdiversity,real-timeresponsiveness, andcontextualunderstanding,thesystemcanbedeployedin assistivecommunicationtools,videoconferencingplatforms, andsmartdevices.Theseadvancements willcontributeto greaterinclusivityandenhancedcommunicationforthedeaf andhard-of-hearingcommunity.
Fig -2: Performance metrics of Sign Language Detection Model
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN: 2395-0072
REFERENCES
[1]. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556,2014.
[2]. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 770–778, 2016.
[3]. F. Chollet, “Xception: Deep learning with depthwise separableconvolutions,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR),pp.1251–1258,2017.
[4]. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” NeuralComput.,vol.9,no.8,pp.1735–1780, 1997.
[5]. “American Sign Language Dataset,” Kaggle, [Online]. Available:https://www.kaggle.com/databases.
[6]. Zhang and W. Lee, “Sign language recognition using deeplearning:Areview,” IEEE Trans. Multimedia,vol. 23,pp.345–359,2021.
[7]. “American Sign Language Linguistics Research,” National Institute on Deafness and Other CommunicationDisorders(NIDCD),[Online].Available: https://www.nidcd.nih.gov/research/american-signlanguage
[8]. D.P.KingmaandJ.Ba,“Adam:Amethodforstochastic optimization,” arXiv preprint arXiv:1412.6980,2014.
[9]. Mrinal M Prasad, Sreelekshmi B “Sign Language And HandGestureRecognitionSystem”Volume11,Issue7 July2023,pp551-561,2023.