Bridging Communication Gaps: Real‑Time Sign Language

Translation Using Deep Learning and Computer Vision

K. E. Eswari, Rahuls Raja R. V., Vaibhavan M. and Yuva Surya K. S.

Department of Computer Science and Engineering, Nandha Engineering College, Erode, Tamil Nadu, India

Keywords: CNN, Real‑Time Translation, Sign Language, Deep Learning, Computer Vision, Accessibility.

Abstract: Owing to a knowledge gap on sign language, deaf and hearing communities face communication issues. The

rest of the paper aims to give an overview of the topic and present a viable live solution through a Sign

Language Translator based on computer-vision and deep learning. The model utilizes a camera to detect hand

gestures, followed by their analysis (CNN) and ultimately producing voice or text. Recognition performance

is improved by a large dataset and sentence generation is augmented by NLP methods. This technology is

built for changing environments and compensates for occlusion, speed or lighting differences. The excellent

accuracy in both experiments indicates that it can be an exciting tool for inclusion.

1 INTRODUCTION

Sign language, communication would not be

effective among those who are deaf and hard of

hearing, but the general public is not widely aware of

it, thus creating barriers, social isolation, and difficult

access to services. The advances in deep learning and

computer vision have enabled real-time sign

language translation in a portable form-factor by

overcoming the limitations of glove- and sensor-

based methods, which are intrusive and expensive.

This paper presents a sign language translator

system which combines gesture recognition capabili-

ties of deep learning with speech and text

transformation by Natural Language Processing

(NLP) in real time. Additionally, the CNNs

accurately identify and classify hand gestures by

offering consistent performance regardless of the

varying conditions of the environment, such as hand

position, lighting and background. Using a normal

camera to capture the gestures, the approach is cost-

effective and is scalable. Signers and non-signers can

find easier ways to communicate with one another,

thanks to complex computer vision and deep

learning techniques that enable this technique of

recognition. The proposed method successfully

bridges the gap in communication, offering a

convenient and adaptable tool for real-time

translation.

2 RELATED WORKS

Deaf and hard of hearing heavily depend on sign

Language but low public awareness has led to

barriers, social isolation, and limited access to

services. Recent years have seen great strides in deep

learning and computer vision, enabling real-time sign

language translation that does not rely on a glove- or

sensor-based method that is obtrusive and expensive.

The objective of this paper is to devise a deep

learning-based gesture recognition and NLP based

conversion for sign language translation system

which works in real time. CNNs accurately detects

and recognizes hand movements, ensuring consistent

performance across different environments

irrespective of hand position, light condition or

background. It is a cost- effective and scalable

solution as the system utilizes an ordinary camera to

These last two capabilities enable signers and non-

signers to interact with each other by a combination

of advanced computer vision and deep learning

techniques, ultimately making the non-

signers a part of the conversation. This proposed

system effectively bridges the communication gap by

ensuring a simple and versatile tool for quick

translation.

Eswari, K. E., V., R. R. R., M., V. and S., Y. S. K.

Bridging Communication Gaps: Real-Time Sign Language Translation Using Deep Learning and Computer Vision.

DOI: 10.5220/0013899700004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

443-447

ISBN: 978-989-758-777-1

443

3 DATASET COLLECTION AND

PRE-PROCESSING

3.1 Dataset Collection

Sign language is essential for communication among

deaf and hard of hearing individuals but far too

uncommon for public awareness, resulting in barriers,

social isolation and challenges accessing services.

Advances in deep learning and computer vision now

enable real-time sign language translation,

eliminating limitations of glove- and sensor-based

methods, which are intrusive and expensive. Use of

Deep Learning and NLP Based Real Time Sign

Language Translation System. CNNs detect and

classify hand gestures with accuracy, yielding

consistent performance across different conditions

with varying hand positions, lighting, and

backgrounds. It is a cost- effective and scalable

solution since the system leverages an ordinary

camera to record gestures. By using advanced

computer vision and deep learning algorithms, the

process enables communication between signers and

non-signers, promoting inclusivity and

understanding. This proposed system minimizes the

barrier in communication and serves as a convenient

and versatile tool for real-time conversion.

3.2 Data Pre-Processing

Data Pre-processing Data pre-processing is a crucial

step for the real-time sign language translator to

function correctly and accurately. The deep learning

model receives the raw dataset as hand motion

videos and pictures, but before that, there are a few

steps to preprocess this data, to remove inconsistency,

minimize noise and improve quality. The first step

involves data cleaning and normalization, i.e.,

excluding fuzzy or low-quality images and

eliminating unwanted frames from video sequences.

Since hand position, lighting changes, and

background can affect recognition, Histogram

equalization and contrast stretching are applied to

ensure uniform image quality. Background

subtraction and image segmentation techniques are

applied to emphasize the hand movements.

Background removal and skin detection methods

based on deep learning help isolate isolated hands

from the surrounding environment and reduce

interference from external objects. For uniformity

across the collection, each frame and image are also

resized to a specified size. To enhance the model's

ability to identify similar motions, edge detection

techniques like Canny and Sobel filters are applied to

extract vital information such as hand shapes, edges,

and motion patterns. The use of data augmentation

methods such as rotation, flipping, scaling, and

brightness adjustments enhances generalization.

These techniques make the model robust to the real-

world by exposing it to both subtle variations in hand

trajectories and illumination changes. These pre-

processing techniques make dataset cleaner, more

organized and suited for efficient gesture recognition

improving the performance of the system as second

step.

4 PROPOSED METHODOLOGY

In order to detect hand gestures accurately and

convert these gestures into text or voice, a full real-

time sign language translation system that combined

deep learning and computer vision methods was

proposed (Verma and P. Singh, 2023) Some of the

key steps involved in the methodology are- real-time

gesture recognition, data acquisition, model training

and natural language processing (NLP) for speech to

text and text to speech. Every step is vital to

guarantee that the system is accurate and effective.

4.1 Pre-Processing and Data

Acquisition

First, a standard RGB camera is used to capture hand

movements. This dataset includes existing publicly

available sign language datasets and a set of signs

collected by custom-compiled data of a diverse

individuals with different ethnicities, skin tones, and

hand sizes to improve generalization. Right from the

data acquisition the noise reduction and contrast

enhancement and segmentation techniques and

background removal techniques are preprocessed

before it is finally fed to a model. Additionally, it is

insensitive to various background conditions, hand

positions, and illumination levels through the data

augmentation methods of rotation, scaling, flipping,

and brightness.

4.2 Using Deep Learning for Gesture

Recognition

The system utilizes a trained CNN with labelled

photos of hand gestures that can achieve an accurate

hand gesture recognition. Pre-trained deep learning-

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

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based architecture used for sign recognition has been

a ResNet or MobileNet model which uses the

different signs that were identified according to the

spatial features such as the shapes of the hands, the

edges and the movement patterns. The model is

trained on a large dataset and the hyperparameters

such as learning rate, batch size and activation

functions are tuned to get better accuracy. This is

similar to the usage of LSTM networks to get

continuous sign language recognition in dynamic

movements which require sequential processing.

4.3 Real-Time Classification and

Detection of Gestures

Following training, the model is employed to detect

gestures in real time. The CNN model is employed to

classify the gesture once the system handles live

video data from a camera and utilizes image

processing to detect the region of the hand. Even in

cases of fast hand movement or short-term

occlusions, tracking functionality ensures gestures

are properly recognized. Effective model inference

methods, like Tensors or Open VINO, boost

processing speed without compromising accuracy,

enabling real-time performance optimization.

4.4 Converting Text and Speech Using

Natural Language Processing

Upon identification of the gesture, the respective sign

is translated into text with structure with the help of

Natural Language Processing (NLP). As sign

language is structurally and grammatically different

from oral language, an NLP model rewrites the

translated sentences in grammatical order. Through

this process, communication is amplified as the

resultant output is easily comprehensible by non-

signers. Additionally, a text-to- speech engine is

included for offering speech output, thus facilitating

the system's use by people who are used to auditory

interaction. Sophisticated NLP models like

Transformer-based models (e.g., BERT or GPT) can

be used to enhance sentence formation and contextual

comprehension.

4.5 System Installation and Interface

The final step involves deploying the NLP engine and

the trained model in an easy-to-use application. The

system is deployable either as a web application or as

a mobile application, allowing ease of user

interaction. Real-time gesture detection, live text and

voice translation, and sign language customization

are all features of the interface. Due to its

optimization, the application provides a latency-free

user experience with low latency.

4.6 Assessment and Improvement of

Performance

The efficiency of the system is confirmed by

extensive testing with real-world applications. It

tests key performance metrics including robustness,

accuracy, latency and user satisfaction. Latency is

another aspect that

is tested to provide a responsive

feel for real-time use, and accuracy is measured by

looking at the predicted gestures and comparing them

to their ground truth label. Frequent

optimisations of

the inference pipeline and fine-tuning of the model

improve overall system performance.

The authors introduce an integrated framework

combining computer vision, natural language

processing, and deep learning to provide a sign

language translation system such that non-signers and

sign language users are able to communicate. By

utilizing CNNs for gesture recognition, NLP for the

various structure of phrase, and TTS for speech

output, the system guarantees accurate and real-time

translation in diverse situations. Incorporating this

technology into web and mobile applications, leads

to improved accessibility and communication for the

deaf and hard-of-hearing community.

The proposed real- time sign language translation

system effectively bridges the gap of communication

between sign language users and non- signers by

making use of computer vision and deep learning.

Other possible components include Natural Language

Processing for structured text and speech translation,

and of course, Convolutional Neural Networks for

gesture recognition, which makes the whole system

highly accurate and reasonably fast. Because of its

wonderful classification accuracy, least latency, and

environmental adaptiveness, it is the feasible solution

for use in real-life applications, based on test results.

The technology also makes deaf or hard-of-hearing

people more accessible by offering a low-cost,

scalable, and unobtrusive solution for conventional

sensor-based approaches. NLP enables the translated

text to be coherent and meaningful by refining the

structure of the sentences. Also, seamless

communication is ensured by real- time processing,

allowing natural user interaction. Although the

performance of the system has been promising, it

could be made even more efficient with more features

such as support for multiple sign languages and

Bridging Communication Gaps: Real-Time Sign Language Translation Using Deep Learning and Computer Vision

445

improved recognition in challenging conditions.

Future research could focus on integrating body

posture and facial expression recognition to enhance

translation accuracy. All things considered, by

enabling the deaf community and promoting

accessibility through innovative AI-based solutions,

this initiative promotes inclusive communication.

Figure 1 shows the Flow of detection.

Figure 1: Flow of Detection.

5 EXPERIMENTAL RESULT

The robustness, speed of processing, and accuracy of

the real-time sign language system in different

environments were evaluated. The model was trained

and tested using a set of different hand gestures, and it

had an average classification accuracy of (2.8% for

dynamic and 95.2% for static signs. The adaptability

of the system was tested in different lighting

conditions, with different backgrounds and hand

orientations. The model performed well in controlled

environments, the results showed, although its

accuracy was slightly reduced in dense backgrounds

and low light. The system provided an average delay

of 30 milliseconds per frame, ensuring smooth real-

time translation, based on the frame processing speed

employed to evaluate real-time performance.

Sentence reordering through natural language

processing (NLP) enhanced fluency in translation and

generated more readable and natural-sounding text

output. 87% of users who took part in user testing,

both sign language users and non-signers, indicated

more efficient communication.

Overall, the experimental results confirm that the

proposed system effectively translates sign language

movements into speech and text with low latency and

high accuracy. Further improvements can enhance the

usability of the model in the real world, for example,

by making it more robust to background noise and

accelerating inference.

6 CONCLUSIONS

This paper presenting a valuable and technically

elegant solution to the long-standing communication

barrier confronted by the deaf and hearing-impaired

society. This system is a non-intrusive method that is

capable of real-time translation of sign language

using deep learning, computer vision, and NLU

which can be cost-effective and scalable in

comparison to traditional sensor-based methods. The

model shows significant potential for deployment in

the wild with high accuracy, low latency, and

positive user evaluation.

However, work in the future should plan on to

widen multi-lingual sign language support, include

recognition of facial expression and body posture,

and increase executing capacity in complex

surroundings. In conclusion, this paper is an

important contribution for the assistive technology

community and makes an important step towards

accessible and inclusive communication for people

with disabilities.

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