Instantaneous Sign Language Interpretation Leveraging Advanced

Neural Networks

Ulenda Deepika, Shaik Muskan Tahseen, M. Bala Krishna,

Syeda Adeeba Samreen and Nallagoti Nissitha

Department of Computer Science Engineering, Ravindra College of Engineering for Women, Kurnool, Andhra Pradesh,

India

Keywords: Sign Language, Deep Learning, Communication Barriers, Gesture Recognition, Convolutional Neural

Networks (CNNs), Recurrent Neural Networks (RNNs), Computer Vision, Hand Gestures, Facial

Expressions, Body Gestures.

Abstract: Native sign language is most commonly used by hearing disabled individuals for communication Deep

learning algorithms are making way for motion and gesture detection and classification. This topic is getting

more popular due to advancement in deep learning techniques and computer vision. CNN’s assistance toward

closing the gap between signers and non-signers. A web app is created with the trained model to make it

publicly available. When input this image, it has used computer vision and neural network and detects the

signs and gives the respective text as output. The recognized gestures are then converted into text or voice

output, which can be used to display on the screen, or using some speaker to speak. The system captures video

frames of sign language gestures using a camera. In this paper, we are developing a sign language recognition

system using deep learning techniques in real time. The system is an intended solution to a problem by

providing real-time translation of sign language gestures to either text or speech, serving to bridge the gap

between deaf and hearing individuals.

1 INTRODUCTION

Sign language is the primary method of

communication among hearing and speech impaired

individuals. Since the public has little understanding

about sign languages, this poses many barriers for

individuals in interaction or inclusivity in the broader

scheme of life. The challenge that needs to be

addresses with his gap requires something robust,

efficient, and accessible for it to be successful in

translation; sign language can be interpreted as

spoken pr written text.

Novel gesture recognition applications and

innovative applications from recent advancements in

deep learning and computer vision pave the way

toward effective recognition. Deep learning models,

especially the CNNs and RNNs, have performed very

well and are highly reliable in recognizing the

complex patterns present in hand movement, facial

expression, and even gestures of the whole body. In

video streams, these models may extract meaningful

features and classify them into predefined categories

corresponding to a sign language gesture.

Unlike traditional methods that rely on human-

crafted features, these methods can automatically

learn complex patterns and representations from large

datasets of sign language videos, resulting in better

accuracy and robustness.

In this Paper, a real-time sign language

recognition system based on deep learning-based

models is proposed to effectively convert sign

language gestures to text or spoken words, to improve

communication and understanding between deaf and

hearing communities.

2 RELATED WORKS

The Sign Language Recognition System represents a

transformative convergence of human cognition and

technological innovation, enabling real-time

recognition of sign language using advanced machine

learning techniques (Pavlovic et al.). Neural network

700

Deepika, U., Tahseen, S. M., Krishna, M. B., Samreen, S. A. and Nissitha, N.

Instantaneous Sign Language Interpretation Leveraging Advanced Neural Networks.

DOI: 10.5220/0013904100004919

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

700-706

ISBN: 978-989-758-777-1

architectures such as Convolutional Neural Networks

(CNNs) and Long Short-Term Memory (LSTM)

models have made it possible to translate gestures

into text with near-human accuracy, approximating

spoken language semantics (Sun et al.; Wang et al.).

One notable system introduced at the UNI-TEAS

2024 conference translates spoken input to text and

then into International Sign Language (ISL). It

operates offline and predicts signs or phrases based

on real-time video feed, achieving ~100% accuracy

in sign recognition and ~96% for phrase structures

(Goyal & Singh; Wang et al.).

A significant contribution to Indian Sign

Language research utilized MobileNetV2 with

transfer learning for accurate ISL gesture recognition,

aiming to improve accessibility for the hearing-

impaired in India (Karishma & Singh; Goyal &

Singh). Additionally, CNNs have been combined

with Generative Adversarial Networks (GANs) for

both recognition and video generation of ISL signs,

resulting in high-quality outputs as shown by a PSNR

of 31.14 dB and an SSIM of 0.9916 (Khadhraoui et

al.; Ni et al.). These metrics confirm minimal

distortion and high fidelity in the generated video

content.

Systems that recognize sign language using

depth-sensing tools like Microsoft Kinect have shown

effective alphabet recognition, further supporting

real-time communication (Dong et al.). Hand gesture

datasets, such as the 2D ASL dataset proposed by

Barczak et al., have served as foundational resources

for gesture training and classification. Furthermore,

gesture-based systems have been explored for

enhancing decision support in sports through visual

cues (Bhansali & Narvekar), and wearable

technologies like ISL-to-speech gloves have

demonstrated real-world applicability (Heera et al.).

Mobile applications integrating image processing

for sign translation provide scalable, platform-

independent solutions for American Sign Language

(Jin et al.). Likewise, early research into gesture

interfaces laid the groundwork for modern deep

learning-based systems (Lesha et al.). Altogether,

these efforts underscore the evolution of sign

language recognition from static image interpretation

to dynamic, intelligent systems capable of real-time

translation and inclusive communication (Murthy et

al.).

3 METHODOLOGY

3.1 Data Sets

• Feature Extraction: Relevant features such as

hand shape, finger orientation, and joint positions

need to be extracted from the hand images.

• Normalization: Normalized the features that

have been extracted in order to standardize input

to the model.

• Deep Learning Model Selection: CNN for

Spatial Features: Perform convolutional neural

networks to extract spatial features of hand

gestures in every frame. RNN for Temporal

Features: Implement the RNNs such as LSTMs

to describe the temporal dynamics that

encompass the sign language gestures in several

frames.

• Training the Model:

• Dataset Splitting: Divide the collected dataset

into a training set, a testing set as well as a

validation set.

• Training the Network: For the CNN or RNN

model, train on the training data and optimize the

parameters so that the gesture in the sign

language can be classified most accurately.

• Hyperparameter Tuning: Learn adjustment of

learning rate, batch size, and other

hyperparameters to improve model performance.

• Real-time Inference: Frame by frame analysis,

Frame extraction, Features extracted from the

Frame, Feature extracted on every frame is feed

into the learnt model for prediction of

corresponding sign language gesture.

• Translates the recognized signs into text or

speech output, Important Consideration, Sign

Language Variation, Consider the specific sign

language dialect when gathering data and

training the model.

• Robustness to Noise: Implement ways of dealing

with changes in lighting, background clutter, and

hand movements.

• A detector detects a number in this sign language

recognition system which can be easily extended

to cover a wide range of other signs and hands

sign including alphabets. In the model that we

use to develop our system, we are employing a

machine learning model known as CNN.

• With the camera turned on, the user can make

hand signs, and the system will decode the sign

and display it for the user. Making hand signs,

the person can send out a lot of information in a

short period of time. Sign language recognition

Instantaneous Sign Language Interpretation Leveraging Advanced Neural Networks

701

is a great help system for deaf-mute people,

which has been studied for many years. The sad

fact is, every research has some limitations and

yet they are not able to commercialize it.

3.2 Data Preprocessing

Real-time sign language recognition using deep

learning methodology generally comprises the

following: Capture video data from a signer, pre-

process the frames to isolate hand gestures, extract

features using a convolutional neural network (CNN)

to identify key hand shapes and movements, and

finally classify gestures in real time using a trained

model that can translate sign language into text or

speech.

Communication with a person who has a hearing

disability might not be that easy. It is the best medium

for deaf and mute people to tell their stories to their

thoughts and feelings. But simply devising sign

language isn’t enough. Automatic sign gesture

detection systems can bridge the communication gap

that has existed for ages. Finally, the paper presents a

DL-based SLR solution based on two classifiers and

a hybrid optimization algorithm. Using such a hybrid

model improves recognition accuracy and gives the

ability to handle variability in the execution of sign

language.

3.3 Model Architecture

Figure 1: Sign language gesture recognition system.

Hands Tracking: Tracking describes how movement

of the signer's hands is followed over a sequence of

frames. This captures the dynamic nature of sign

language since the movement, position and

orientation of hands over time are all meaningful

parts of a sign.

Feature Extraction: This involves extracting and

measuring the most salient characteristics of the

segmented hand shapes and motions.

Hand orientation: The angle that the hand has with

respect to the body or the camera.

Hand location and movement trajectory: The path

of the hand as it moves through space.

Database: Large number of sign language

examples (recording of hand movements and

positions, corresponding to the linguistic data) to be

trained on as well as test. Figure 1 shows Sign

Language Gesture Recognition System.

Neural Network: Machine learning models used

are of power because the task involved requires

extraction of the most complex patterns from features

associated with certain signs; thus, neural networks

best work when applied.

3.4 Training

Data Acquisition refers to the process of capturing the

hand images representing different signs. The system

is trained and tested on a publicly available data set.

The dataset consists of 9 classes in total. The signs

represented in this dataset are Friends, Help, More,

Yes, Sorry, No, Please, Stay, Again/Repeat. The

dataset comprises a multitude of image collections of

Emblem gestures that were recorded and used to

render realistic scenes.

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Figure 2: Hand gesture recognition dataset for sign language.

Most gestures in the original images render

several expressions in sign language. Every picture

has a noise-like, blurred, messy and colourful

background, such that people can appear to be in real

life communication image scene. These sign gestures

are known as ‘Emblems’, that are known for their

direct and specific meaning. These gestures can often

be used as a substitute for a spoken word; essentially,

they are fully formed signs with a clear translation in

the signed language.

Figure 2 shows Hand Gesture

Recognition Dataset for Sign Language.

Image Processing: Before performing any task

like sign language recognition, image preprocessing

is a must. Similar to cleaning and segregating data

before analysis, pre-processing makes sure that the

images are in the format required by the computer

vision model. This could mean cutting out noisy

backgrounds or resizing images to the same sizes.

Preprocessing is a necessary step because it improves

the quality of the data that is fed to the model. The

proposed noise-reducing model utilizes the selected

salient features of the image to provide a more

accurate representation of input data.

Normalization: Images are typically stored as

grids and pixels, with each pixel quantified by its

numeric value and its colour intensity. These values

depend on the format of the image. Using raw data

led to challenges for CNN:

• Inconsistent data

• Activation Function

Resizing: Using data without resizing the

information appropriately may cause various issues

when preparing the CNN, such as computational

complexity and difficulties in learning patterns.

4 RESULTS AND DISCUSSION

Real-time sign language translation via experimental

learning in deep learning.

Experimental learning in this context involves

designing and conducting systematic experiments to

construct and test a real-time sign language

translation system. Here's a structured approach for

such a design:

Define Objectives: Translates real-time sign

language gestures into text or speech. Identify static

and dynamic gestures by using deep learning.

Achieve high precision while maintaining the low

latency for the real-time application.

Here Accuracy curve shows the contrast between

the Training Accuracy and Validation Accuracy,

Validation Data Signed Distribution shows the

Validation Data Signed Distribution, Year of

Publications It shows the increasing number of

Publications and Cumulative Publications,

Instantaneous Sign Language Interpretation Leveraging Advanced Neural Networks

703

Enhancing the applicability of sign language

translation.

4.1 The Discussion of Findings

The real-time sign language translator using deep

learning shows great promise in filling gaps in

communication while maintaining high accuracy in

controlled settings. However, variability in signing

styles, lighting, occlusions, and latency issues all

detract from the performance of real-world systems.

Static gestures were well recognized, but dynamic

and complex gestures are problematic. User feedback

emphasized the need for broader sign language

support, multi-modal integration, and improved

usability in diverse settings. These insights

underscore the importance of dataset diversity, model

optimization, and user-centric enhancements to make

the system more robust and inclusive.

4.2 Recommendation for Future Work

Future development of the real-time sign language

translator should be focused on practical application

challenges. More diverse and inclusive datasets must

be collected, including regional sign variations and

gestures performed in real-world conditions.

Usability will greatly benefit from advances in

algorithm design for complex and dynamic gestures

as well as improvements in the responsiveness of the

system on a variety of hardware. Integrating facial

expression recognition and user-definable gestures

would be much easier. Testing the system across a

range of settings with end-users will be critical

together feedback which will be necessary to

optimize the system and to ensure that it is within the

capacity of the diverse communities that might need

to use it.

4.3 Performance Evaluation

You can train on data till October 2023. Energy

performance needed to analyse gesture recognition

accuracy & real time performance of system.

Because of the requirements for gesture observation

in real time, crucial metrics like accuracy, precision,

recall and F1 score were used to establish how

successfully the system recognizes gestures and

avoids false alarms whilst FPS and latency are a must

for seamless translation in real-time. Moreover, it

needs to be tested across different environments with

different light, background, and different signing

styles for it to be robust. We want to obtain the

knowledge about the usability part of the system and

whether it satisfies the requests of sign language

users, replies obtained from user feedback. By

carefully examining these elements in this way, the

system can be tuned for better dependability and

performance in a number of scenarios.

4.3.1 Accuracy

The real-time sign language translator also holds

accuracy over the recognition care sets of sign

language a word into text or voice. This metric gives

a general indication for how well the system is

working by taking the ratio of correct predictions,

both gestures and non-gestures, made by the model to

the total number of predictions. Typically, the

accuracy of deep learning-based sign language

translator is calculated based on the number of

correct predictions by comparing the model output

with the corresponding gestures in the test dataset.

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =𝑇𝑃+





+𝐹𝑁+𝑇𝑃+𝑇𝑁 (1)

Where:

• TP: True Positives (correctly classified

gestures)

• TN: True Negatives (correctly identified

non-gestures)

• FP: False Positives (incorrectly classified

gestures)

• FN: False Negatives (missed gestures)

4.3.2 Precision

Precision in the context of a real-time sign language

translator model means it is able to identify gestures

accurately once it predicts them. It is very important

in systems where error in gesture recognition might

confuse the parties in communication or

misunderstand what is being communicated. High

precision henceforth ensures that when the system

identifies a gesture, it is almost certain and has fewer

false positives, which are incidents where the system

indicates a non-gesture as a gesture.

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =



+𝐹𝑃 (2)

4.3.3 Recall

Recall, in particular, measures the percentage of true

gestures that the system correctly classified against

the total number of true gestures in the dataset, which

constitutes both correctly and incorrectly recognized

gestures: True Positives, TP, along with False

Negatives, FN.

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704

𝑅𝑒𝑐𝑎𝑙𝑙 =





+𝑇𝑃 (3)

4.3.4 Sensitivity

The instantaneity of sign language interpretation with

sophisticated neural networks is a measure of how

well the system can identify and interpret sign

language gestures in real time with high accuracy and

contextuality. It entails the identification of complex

hand and finger motions, comprehension of facial

expressions that are part of meaning, and

differentiation between confusingly similar signs

with context.

4.3.5 Specificity

Specificity measures the proficiency of a live sign

language to translate correctly; it identifies any

frames that might not correspond with any gesture:

distinguishing non-gesture frames from a gesture.

That is calculated with the ratio: true negatives

correct identification of being a non-gesture frame,

where the total quantity of non-gesture frames is

obtained by adding true negative and false positives.

𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 =



+𝐹𝑃 (4)

4.3.6 F1-Score

The F1 score is a measure that combines precision

and recall for an appropriate balanced measure of the

performance of a model, especially when the data is

imbalanced. Precision is how many of the predicted

gestures are correct, and recall is how many actual

gestures were correctly identified.

𝐹1 𝑆𝑐𝑜𝑟𝑒 =2⋅Precision+

𝑅𝑒𝑐𝑎𝑙



𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ⋅Recall





(5)

4.3.7 Mean Absolute Error (MAE)

MAE is a measurement that evaluates a model's

estimation accuracy when its system outputs continue

values, say, numerical values of probabilities

corresponding to gestures and their translations. It

computes an average of the differences between the

obtained and actual values as their absolute values. In

the case of a real-time sign language translator, if the

system predicts a numerical probability or a

translation score for a certain gesture, MAE helps

measure how far off those predictions are from the

true value.

5 DATA OVERVIEWS

A significant improvement in the communication of

the deaf or hard of hearing people with others who do

not understand sign language is potential through sign

language translation systems. These systems can

recognize gestures performed with the hands and

translate them into something other people can

comprehend, such as text or speech. What makes the

systems so strong is that they adapt to styles of

signing. People do not all sign alike, and different

signs can carry the same meaning. They are also

sensitive to the movement of the body and facial

expressions.

This is one of the key elements in conveying

meaning. Despite the challenges in further developing

this technology - for example, the noise with low light

data environments- this technology will advance to

make the world a more inclusive place. It will help

ease the communication barriers that create isolation

in this increasingly global world

6 CONCLUSIONS

Real-time sign language translation using deep

learning is one of the interesting developments that

might help bridge the communication gap between

deaf and hearing individuals. In the future, this

technology is likely to be able to make conversations

more fluid and inclusive and allow people to express

themselves freely without language barriers.

Although there are still many challenges to be

overcome, such as accuracy in difficult conditions,

the system's ability to interpret a wide variety of

gestures, the progress that has been shown so far is

almost promising. Further research and development

will move this technology toward a world where

people can communicate freely and effectively

regardless of their degree of hearing loss.

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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