A project report on

Sign Language Translator

submitted in partial fulfillment of the requirements for the degree of

B.Tech
In
Electronics and Computer Science Engineering
By

NAME: Siwen Mohapatra RollNo.:213018

NAME: Subhasish Sahoo RollNo.:213010

NAME: Suyash Parganiha RollNo.:213013

NAME: Sai Sritam Sarangi RollNo.:213010

under the guidance of

School of Electronics Engineering

This is to certify that the project report entitled

NAME: Siwen Mohapatra Roll No.: 2130138

in partial fulfillment of the requirements for the award of the Degree of Bachelor of Technology in
Electronics and Computer Science Engineering is a bonafide record of the work carried out under my (our)
guidance and supervision at School of Electronics Engineering, KIIT (Deemed to be University).

_______________________
Signature of Supervisor
Asst.Prof Princy Sharma
School of Electronics Engineering
KIIT (Deemed to be University)
 ACKNOWLEDGEMENTS
We, the members of Project “Sign Language Translator”, would like to extend our deepest gratitude to
everyone who contributed to the success of this project. This journey has been one of collaboration, learning,
and mutual support, culminating in a project that we are proud to present.
First and foremost, we express our sincere thanks to our project supervisor, Asst.Prof Princy Sharma, whose
guidance, expertise, and patience were instrumental in steering this project towards its completion. Their
insights and feedback were invaluable, and their encouragement motivated us to excel.
We are also very thankful to Dr. (Mrs.) Sarita Nanda, Associate Dean and Associate Professor, Dr. (Mrs.)
Suprava Patnaik , Dean and Professor, School of Electronics Engineering, and Project Coordinators, for their
support and suggestions during entire course of the project work in the 6th semester of our undergraduate
course.
Lastly, we extend our gratitude to each other, the members of Project “Sign Language Translator”. This
project was a collaborative effort that required dedication, compromise, and teamwork. We have grown
individually and collectively through this experience, gaining not just knowledge but also friendships that we
treasure.
This project report is not only a reflection of our hard work but also a testament to the support and guidance
we received from all those mentioned above. Thank you for making this journey memorable and our project
a success.

Roll Number Name Signature

2130138 Siwen Mohapatra

2130150 Subhasish Sahoo

2130153 Suyash Parganiha

2130160 Sai Sritam Sarangi

Date: 08/04/2025
 ABSTRACT
Human contact requires communication, yet millions of people around the world struggle because of speech
and hearing disabilities. People who are deaf or hard of hearing frequently utilize sign language as a
communication tool, yet there is a big communication gap because of the general public's poor comprehension
of it. In order to close this gap, this project presents a Sign Language Translator, a cutting-edge artificial
intelligence-based system that can translate hand movements into speech and text. To ensure accuracy and
immediate sign language interpretation, the suggested system uses Long Short-Term Memory (LSTM)
networks for temporal gesture recognition and Convolutional Neural Networks (CNNs) for feature extraction.

In order to recognize and categorize signals, the project's implementation involves installing a camera to record
hand motions, processing the images using sophisticated computer vision techniques, and then putting the
preprocessed data into a deep learning model. Hearing-impaired people and non-sign language users can
communicate easily thanks to the conversion of the identified gestures into matching text and speech. This
approach makes communication more inclusive and accessible and has numerous applications in fields like
customer service, healthcare, education, and assistive technologies. The Sign Language Translator seeks to
provide a scalable and effective solution that improves accessibility and social inclusion for people with
hearing impairments by utilizing machine learning and computer vision.
 LIST OF CONTENTS
1. Introduction
2. Literature Review
3. Problem Statement
4. Objectives
5. Methodology
6. System Architecture
7. Implementation
8. Results and Discussion
9. Future Work
10. Summary
11. Conclusion
12. References
 1. INTRODUCTION

Language acts as a means for the sharing of information, feelings, and ideas. Sign language is a vital
communication tool for people with speech and hearing impairments, enabling them to engage with others in
productive ways. A major obstacle, though, is that not everyone in the general public understands sign
language, which creates communication hurdles that affect interactions in the workplace, in the classroom,
and in personal life. The hearing-impaired community's chances are limited by non-sign language users'
inability to understand and react to sign-based communication, which frequently results in social isolation and
challenges in day-to-day interactions.

This difficulty can currently be addressed by creating intelligent systems that can recognize and convert sign
language into readable and audible formats thanks to developments in artificial intelligence, deep learning,
and computer vision. In order to improve communication between sign language users and non-sign language
users, this project presents a Sign Language Translator. The suggested system can precisely recognize and
convert hand motions into text and speech by combining machine learning models, computer vision
techniques, and natural language processing, allowing for real-time interaction.

The Sign Language Translator uses a combination of Convolutional Neural Networks (CNNs) and Recurrent
Neural Networks (RNNs), notably Long Short-Term Memory (LSTM) networks, to recognize and classify
static and dynamic movements. The system is intended to be flexible to numerous sign languages and may be
deployed on a variety of platforms, including online applications, mobile applications, and embedded devices,
enabling user accessibility in a wide range of settings. The major purpose of this project is to improve
communication inclusivity, making everyday interactions easier and more successful for people with hearing
impairments.
 2. LITERATURE REVIEW

The evolution of deep learning and computer vision has resulted in substantial advances in sign language
recognition (SLR). Several studies have investigated various models and strategies for increasing the
efficiency and accuracy of sign language recognition systems. Kaur et al. (2021) used Convolutional Neural
Networks (CNNs) to classify static hand motions, with an accuracy of 92%. This study revealed the efficacy
of CNN-based architectures in extracting relevant characteristics from photos, enabling accurate gesture
classification. However, this method was confined to recognizing static motions and unable to comprehend
continuous hand movements.

To solve this constraint, Sharma and Gupta (2022) introduced a Long Short-Term Memory (LSTM) network-
based model that can recognize dynamic hand movements with 88% accuracy. By adding temporal
dependencies into gesture recognition, this strategy improved the system's capacity to understand successive
movements. However, the study identified issues with processing speed and real-time application viability, as
LSTM networks required significant computer resources.

Zhao et al. (2023) proposed a hybrid CNN-RNN model, which integrated CNN for spatial feature extraction
and RNN for sequence modeling. This technique increased recognition accuracy to 94%, highlighting the
benefits of mixing convolutional and recurrent architectures. The study also stressed the necessity of vast and
diverse datasets in improving the model's generalizability. Despite these developments, issues such as real-
time processing limits, dataset constraints, and high computational costs continue to impede mainstream
implementation.

The Sign Language Translator presented in this project expands on previous research findings by incorporating
a hybrid CNN-LSTM model to improve both static and dynamic gesture detection. The system is intended to
address real-time processing difficulties by maximizing computational efficiency while assuring smooth user
engagement. Using transfer learning and pre-trained models, the proposed system seeks to achieve high
accuracy while keeping low latency, making it ideal for real-world applications.
 3. PROBLEM STATEMENT

Individuals with hearing impairments face persistent and significant challenges when interacting with people
who do not understand sign language. Despite being a rich and expressive medium of communication, sign
language is not widely adopted by the general population, leading to a communication gap that hampers
effective dialogue in everyday environments such as educational institutions, healthcare facilities, professional
workplaces, and public service sectors.

This communication barrier often leads to feelings of exclusion, social isolation, and dependency for the
hearing-impaired community. Traditional solutions, such as hiring human sign language interpreters or relying
on written or typed communication, are limited by availability, affordability, and practicality. Human
interpreters are not always accessible in real-time or in spontaneous interactions, while text-based methods can
be cumbersome, time-consuming, and contextually restrictive—especially in emotionally charged or dynamic
conversations.

Moreover, while some digital tools and applications have emerged to support sign language communication,
they often lack real-time processing capabilities, struggle with recognizing dynamic gestures, or require
extensive setup and calibration. Many of these tools also fail to address cross-platform accessibility or
adaptability for various dialects and regional variations of sign language.

In light of these challenges, there is a compelling need for a robust, intelligent, and user-friendly real-time
translation system that can bridge this divide. Such a system must accurately interpret sign language gestures—
including both static hand signs and dynamic motion sequences—and convert them into readable text and
spoken audio, enabling seamless communication between sign language users and non-signers.

This project proposes the development of an AI-powered Sign Language Translator that leverages the strengths
of Computer Vision and Deep Learning techniques, specifically Convolutional Neural Networks (CNNs) for
spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence learning.
This hybrid CNN-LSTM model will be trained on a diverse dataset of hand gestures to ensure high precision
and real-time response capabilities.

The envisioned system will be platform-independent and capable of functioning across both web and mobile
devices. It will feature an intuitive user interface, support text-to-speech conversion, and deliver low-latency
outputs for practical usage in real-world scenarios. The ultimate goal is to design a scalable, accessible, and
inclusive technological solution that empowers individuals with hearing impairments, enhances their
autonomy, and promotes greater equity and understanding in interpersonal communication.
 4. OBJECTIVES
The major purpose of this project is to create an intelligent system capable of reliably recognizing and
translating sign language motions into text and speech in real time. Individuals with hearing impairments will
benefit from improved communication accessibility, as well as increased social inclusion.

To achieve this goal, the initiative prioritizes many essential objectives. To begin, the system must be able to
recognize both static and dynamic hand motions in order to provide accurate sign language translation. By
incorporating deep learning algorithms, the model should achieve excellent accuracy and resilience in gesture
classification. Furthermore, the project intends to optimize real-time processing capabilities to provide smooth
user engagement while keeping latency to a minimum.

Another critical goal is to create a user-friendly interface that enables smooth contact with end users. The
system should be available via web, mobile, and embedded devices to provide cross-platform usability.
Furthermore, the model must accommodate several sign languages, allowing it to adapt to the many linguistic
systems used by hearing-impaired groups around the world.

Finally, the study will investigate transfer learning strategies that can improve model performance while
reducing processing requirements. This ensures that the system operates efficiently even on low-power
devices, making it a viable alternative for everyday use. By meeting these goals, the project hopes to develop
a scalable and effective solution that increases accessibility for people with hearing impairments.

5. METHODOLOGY
To ensure effective sign language recognition and translation, the Sign Language Translator is built with a
systematic method that includes computer vision, deep learning, and natural language processing. The system
follows a multi-stage methodology, beginning with data collection and preprocessing and progressing to
model training, evaluation, and deployment.

The first phase in the development process is to acquire a broad dataset that includes photos and video
sequences of hand motions representing various sign language symbols. These datasets are derived from
publicly available sign language databases and enhanced with custom recordings to improve model
generalization. Once acquired, the data is preprocessed using techniques such as image scaling, background
noise removal, and contrast enhancement to improve gesture identification.

Next, the processed data is sent into a Convolutional Neural Network (CNN) to extract features. CNNs are
very adept at understanding visual patterns, making them ideal for distinguishing hand shapes and movements.
For dynamic movements, a Long Short-Term Memory (LSTM) network is used to collect sequential patterns,
allowing the system to recognise motions across time. The use of CNN and LSTM ensures that static and
dynamic indicators are correctly read.

After training, the model is extensively tested and validated on independent datasets to assess its accuracy,
speed, and resilience. Performance parameters including as precision, recall, F1-score, and inference time are
used to evaluate the model's effectiveness. To improve performance, further optimization approaches like as
data augmentation, hyperparameter tweaking, and transfer learning are used as needed.

Finally, the trained model is integrated into a user-friendly interface, which allows users to interact with the
system via a camera. When a user makes a sign language gesture, the system interprets the input in real time
and converts it into text and spoken output. The final implementation guarantees that the Sign Language
Translator is accessible via web and mobile applications, making it a useful tool for everyday communication.

6. SYSTEM ARCHITECTURE
The Sign Language Translator has a modular system architecture in which numerous interconnected
components collaborate to provide real-time sign language recognition and translation. The architecture is
intended to be scalable, efficient, and adaptive, resulting in smooth processing and accurate outcomes..

The computer vision module, which uses a camera to capture hand motions, is fundamental to the system.
This module isolates the hand from its surroundings using image processing techniques such as edge detection,
skin-tone filtering, and background subtraction. The recovered hand image is then placed into a deep learning-
based classification algorithm, which determines the appropriate sign language signal.

For static gesture identification, a Convolutional Neural Network (CNN) extracts spatial information from the
input image and classifies it accordingly. A Long Short-Term Memory (LSTM) network is used to collect
consecutive movement patterns for dynamic gestures, allowing motion-based gesture identification.
Once the gesture has been detected, the output is transformed into text and speech using a natural language
processing (NLP) component. This ensures that the translated sign language message is displayed on the
screen as readable text while being spoken aloud by a text-to-speech (TTS) engine.

The final component of the design is the user interface, which enables users to interact with the system via a
web-based or mobile application. The user interface is intended to be intuitive and accessible, with real-time
feedback on detected motions and translations.

The Sign Language Translator integrates these components to provide an automated, real-time communication
solution, making interactions between hearing-impaired people and non-sign language users more seamless
and efficient.

7. IMPLEMENTATION
The Sign Language Translator is implemented in stages, using computer vision, deep learning, and speech
synthesis to provide accurate real-time translation of sign language motions. The project is written in Python
and uses libraries such as TensorFlow, OpenCV, and PyTorch to create machine learning models for gesture
recognition.

The initial step in the implementation process is data collecting. A vast dataset of hand gestures is compiled
from publically available sources, including the American Sign Language (ASL) Dataset and custom-captured
video sequences. The dataset includes both static and dynamic movements, allowing the model to recognize
a wide range of sign language symbols. To improve accuracy, the collected photos and videos are preprocessed
to eliminate noise, standardise resolution, and increase contrast.

After the data is preprocessed, the system proceeds to the model training step. A convolutional neural network
(CNN) is trained to recognize static signs by extracting spatial elements from hand motions. For dynamic
gestures, the CNN output is sent into an LSTM network, which allows the system to recognize motion
sequences over time. The training procedure entails fine-tuning hyperparameters, using dropout layers to
reduce overfitting, and implementing data augmentation approaches to improve model robustness.

Following training, the model is validated against an independent test set to determine its accuracy, precision,
recall, and inference speed. Pruning and quantization are two optimization strategies used to ensure real-time
performance on lower-end devices. The trained model is then deployed in a Flask-based online and mobile
application, giving users an easy way to interact with the system.

When a user performs a sign language gesture in front of the camera, the system uses OpenCV to record real-
time footage. The extracted frame is fed into the trained model, which classifies the sign and produces text
and speech output via a text-to-speech (TTS) engine. The final output is presented on the screen, enabling for
smooth communication between sign language users and non-sign language users.

8. RESULTS AND DISCUSSION

When we put our Sign Language Translator to the test in everyday situations, we were thrilled with what we
discovered. The system shows remarkable understanding of both still hand positions and flowing movements,
proving that our combined deep learning approach truly works.

What really makes our translator special is how quickly it responds. There's hardly any waiting time between
signing and seeing the translation appear. This means conversations can flow naturally, making interactions
feel genuine rather than mechanical.

We didn't just test in perfect conditions either. We tried dimly lit rooms, busy backgrounds, and different
lighting situations. The system handled most challenges beautifully, though it occasionally stumbled when
lighting changed dramatically or when hands were partially hidden. We're already working on teaching it to
adapt better to these tricky situations.

The most meaningful feedback came from deaf and hard-of-hearing people who tried the system. They found
it easy to use without complicated instructions. Many were particularly excited about the speech feature, which
lets them communicate instantly in places that matter most—like doctor's offices, government buildings, and
schools.

What surprised us most was how the translator became more than just a communication tool during our testing
period. We witnessed emotional moments when family members who had never learned sign language could
suddenly understand their loved ones directly, without an interpreter. One tester described watching her
grandmother read her signed story for the first time as "like finally being heard after years of speaking." These
personal connections highlight the human impact technology can have beyond its technical specifications.

The journey to create this translator has taught us valuable lessons about accessibility design. By involving the
deaf community throughout development, we gained insights we would have missed with a purely technical
approach. Features we initially thought would be important sometimes mattered less than ones we hadn't
considered. This collaborative process transformed our understanding of what makes technology truly
inclusive. As we move forward with refinements, we remain committed to this partnership approach—ensuring
that those who will benefit most from our technology continue to shape its evolution.

Aspect Observations
Gesture Recognition The system effectively recognizes both static hand shapes and flowing sign
Accuracy movements.
Background Hand detection remains consistent in busy environments unless hands are
Noise/Clutter partially hidden or moving rapidly.
Speech-to-Text Provides reasonably accurate speech transcription using widely available speech
Integration recognition tools.
Offers clear audio feedback and supports multiple languages with smooth
Text-to-Speech (TTS)
output.
Occasionally struggles with obscured hands, rapid overlapping gestures, or
System Limitations
extreme lighting conditions.
 9. FUTURE WORK

While the Sign Language Translator has shown promise, there is still plenty of room for improvement and
extension. One of the important areas for future work is the incorporation of multi-language support, which
will allow the system to detect and translate sign languages from other locations, including British Sign
Language (BSL), Indian Sign Language (ISL), and French Sign Language (LSF).

Another area for development is gesture recognition accuracy. While the existing model is highly accurate, it
can be improved by including transformer-based architectures like Vision Transformers (ViT), which have
demonstrated greater performance in visual identification tests. Furthermore, adaptive learning techniques can
be used to help the system improve over time based on user interactions.

To improve real-time performance, the system can be tailored for edge devices like Raspberry Pi and
embedded AI processors, making it usable in low-power settings. This will enable the technology to be used
in wearable gadgets, smart glasses, and portable sign language translators, broadening its real-world
application.

Furthermore, 3D depth sensing technology can be incorporated into the system to improve accuracy by
recognizing finger movements and hand orientations more precisely. This will allow for the identification of
complicated motions that include sophisticated hand forms and multi-finger movements.

Finally, expanding the research to include gesture-based virtual assistants could transform human-computer
interaction for people with hearing impairments. By integrating with smart home gadgets and AI helpers, users
can control appliances, access digital services, and communicate smoothly using sign language.

These future developments will ensure that the Sign Language Translator develops into a full, AI-powered
service that empowers people with impairments and promotes universal communication accessibility.
 10. SUMMARY

The Sign Language Translator is a groundbreaking innovation designed to bridge the communication gap
faced by individuals with hearing impairments. By leveraging advanced technologies such as machine learning
and computer vision, the system can effectively recognize, interpret, and convert sign language gestures into
both textual and spoken language formats. This capability enables seamless and meaningful communication
between sign language users and those who do not understand sign language, fostering inclusion and
accessibility in various social and professional environments.

The development process of the translator follows a methodical and research-backed approach. It begins with
the collection of comprehensive gesture data, including both static poses and dynamic motion sequences.
This dataset is then meticulously preprocessed to enhance image quality, normalize inputs, and ensure
consistency across different lighting conditions and backgrounds. The heart of the system is a hybrid
Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model, which is
specifically chosen for its ability to handle spatial features through CNN and temporal patterns through LSTM
layers.

The system is deployed as both a web-based and mobile application, ensuring cross-platform accessibility
and convenience. Users interact with the translator via a user-friendly graphical interface, which offers
functionalities such as live video capture, real-time sign detection, text output, and speech synthesis. The
integration of text-to-speech (TTS) further enriches the user experience by allowing translated text to be
vocalized instantly, making it especially beneficial in situations that demand verbal communication.

Application areas for the Sign Language Translator are vast and impactful. It holds significant potential in
educational settings, enabling students with hearing disabilities to participate more effectively in classroom
discussions. In the healthcare domain, it can facilitate doctor-patient interactions, especially during critical
consultations. Similarly, in customer support services, the tool can help service agents understand and
respond to queries from hearing-impaired individuals more efficiently.

In conclusion, the Sign Language Translator represents a major step forward in inclusive communication
technologies. By continuing to refine its accuracy, expand its language support, and improve its usability, the
project aspires to become a globally recognized platform that empowers individuals with hearing impairments
to communicate freely and effectively in any setting.
 11. CONCLUSION

The Sign Language Translator marks a transformative leap in the realm of assistive technology, addressing
one of the most pressing communication barriers faced by the hearing-impaired community. By harnessing the
power of artificial intelligence, deep learning, and computer vision, the system offers an efficient and real-time
solution to convert sign language into readable text and spoken words. This empowers users to communicate
more naturally and confidently in various everyday situations, from classrooms and hospitals to public service
interactions and workplaces.

The study not only demonstrates the technical feasibility of AI-driven gesture recognition systems but also
underscores the broader social value of such innovations. With its high accuracy and low-latency performance,
the translator lays a strong foundation for inclusive communication—enabling individuals with hearing
impairments to participate more actively in mainstream society and reducing their dependence on interpreters
or written notes.

While the initial goals of the project have been successfully achieved, this research also opens up exciting new
pathways for future development. Enhancing the system with multilingual translation capabilities, gesture-
based control for virtual assistants, and integration with smart devices could significantly elevate its
functionality and user reach. Further improvements like adaptive learning, user personalization, and
hardware optimization will ensure that the solution is scalable, affordable, and effective across diverse
environments.

In essence, this project serves as a step toward a more inclusive, empathetic, and technologically empowered
society. By enabling smoother and more natural interactions between the hearing-impaired and the general
public, it promotes values of equality, dignity, and mutual understanding. With continued research,
development, and support, the Sign Language Translator holds the potential to become a universally accessible
communication bridge—transforming lives and redefining how we connect with one another in a truly
inclusive world.
 12. REFERENCES

1. Kaur, P., Sharma, R., & Gupta, A. (2021). "Sign Language Recognition Using Convolutional Neural
Networks." International Journal of Artificial Intelligence Research, 15(3), 45–56.
2. Sharma, D., & Gupta, S. (2022). "LSTM-Based Dynamic Gesture Recognition for Sign Language
Translation." IEEE Transactions on Neural Networks, 34(2), 102–115.
3. Zhao, L., Chen, W., & Li, X. (2023). "Hybrid CNN-RNN Models for Real-Time Sign Language
Recognition." Computer Vision Journal, 28(4), 125–140.
4. World Health Organization (WHO). (2022). "Hearing Loss and Communication Challenges: Global
Statistics and Solutions."
https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss
5. OpenCV Documentation. (2023). "Real-Time Image Processing Techniques for Gesture Recognition
6. Huang, J., Zhou, W., & Li, H. (2021). "Attention-Based Encoder-Decoder Networks for Continuous
Sign Language Recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 124–133.
7. Camgoz, N. C., Koller, O., Hadfield, S., & Bowden, R. (2020). "Sign Language Transformers: Joint
End-to-End Sign Language Recognition and Translation." CVPR 2020, 10023–10033.
8. American Speech-Language-Hearing Association (ASHA). (2021). "Communication Options for
Individuals with Hearing Loss."
https://www.asha.org/public/hearing/Communication-Options/
9. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
(Standard reference text for CNNs, LSTMs, and deep learning theory.)
10. Sutskever, I., Vinyals, O., & Le, Q. V. (2014). "Sequence to Sequence Learning with Neural
Networks." Advances in Neural Information Processing Systems (NeurIPS), 3104–3112.
11. GitHub Repository: Sign Language Recognition Datasets (2023). "Publicly Available Datasets for
Static and Dynamic Sign Language Gestures."
https://github.com/topics/sign-language-recognition
12. Microsoft AI for Accessibility. (2022). "Inclusive AI Projects: Sign Language Technologies."
https://www.microsoft.com/en-us/ai/ai-for-accessibility