A PBL Report

On

SIGN LANGUAGE RECOGNITION

Submitted to CMREC (UGC Autonomous), Affiliated to JNTUH

In Partial Fulfillment of the requirements for the Award of Degree of
BACHELOR OF TECHNOLOGY
IN
COMPUTER SCIENCE AND ENGINEERING
(Artificial Intelligence and Machine Learning)

Submitted By
G. SHASHI KUMAR - 228R1A6685
K. DANIEL BABU - 228R1A6697
K. SOUMYA - 228R1A66A3
V. LAKSHMINARASIMHA SWAMY - 228R1A66C8

Under the guidance of

Mrs. B.REVATHI
Assistant Professor, Department of CSE (AI & ML)

Department of Computer Science & Engineering (AI & ML)

CMR ENGINEERING COLLEGE

UGC AUTONOMOUS
(Accredited by NBA, Approved by AICTE, NEW DELHI, Affiliated to JNTU, Hyderabad)

Kandlakoya, Medchal Road, R.R. Dist. Hyderabad-501 401)

2025-2026
 CMR ENGINEERING COLLEGE
(Accredited by NBA, Approved by AICTE NEW DELHI, Affiliated to JNTU, Hyderabad)
Kandlakoya, Medchal Road, Hyderabad-501 401

Department of Computer Science & Engineering

(Artificial Intelligence and Machine Learning)

CERTIFICATE
This is to certify that the project entitled “SIGN LANGUAGE RECOGNITION” is a
bonafide work carried out by
K.SOUMYA 228R1A66A3

V.LAKSHMINARASIMHA SWAMY 228R1A66C8

K.DANIEL BABU 228R1A6697

G.SHASHI KUMAR 228R1A6685
in partial fulfillment of the requirement for the award of the degree of BACHELOR OF
TECHNOLOGY in COMPUTER SCIENCE AND ENGINEERING (AI & ML) from
CMR Engineering College, under our guidance and supervision.

The results presented in this project have been verified and are found to be
satisfactory. The results embodied in this project have not been submitted to any other
university for the award of any other degree or diploma.

Internal Guide Head of the Department

Mrs.B. Revathi Dr.Madhavi Pingili

Assistant Professor Professor & HOD

Department of CSE(AI & ML), Department of CSE (A&ML),
CMREC, Hyderabad CMREC, Hyderabad
 DECLARATION

This is to certify that the work reported in the present project entitled “Sign Language
Recognition” is a record of bonafide work done by me in the Department of
Computer Science and Engineering(AI & ML), CMR Engineering College. The
reports are based on the project work done entirely by me and not copied from any
other source. I submit my project for further development by any interested students
who share similar interests to improve the project in the future.The results embodied in
this project report have not been submitted to any other University or Institute for the
award of any degree or diploma to the best of our knowledge and belief.

K.SOUMYA 228R1A66A3
V.LAKSHMINARASIMHA SWAMY 228R1A66C8
K.DANIEL BABU 228R1A6697
G.SHASHI KUMAR 228R1A6695
 CONTENTS

TOPIC: PAGE NO

1. INTRODUCTION 1

2. PROBLEM STATEMENT 1

3. EXISTING SYSTEM 1-2

4. PROPOSED SYSTEM 2-4

5. ALGORITHMS 4-5

6.APPROACH 6-7

7. CODE 7-9

8.OUTPUT 9

9. CONCLUSION 10

10. FUTURE ENHANCEMENTS 11

1. Introduction

Sign Language Recognition (SLR) is an advanced technology that enables computers to

interpret human hand gestures and translate them into meaningful text or speech. It is designed
to help bridge the communication gap between the deaf and hard-of-hearing community and the
hearing population. Using Computer Vision and Machine Learning, SLR systems capture
images or video of hand gestures, preprocess the data, extract features such as finger positions
and hand shapes, and then classify them into corresponding words or letters. This system
provides an innovative way to make communication more inclusive and accessible in daily life.

2. Problem Statement
Communication between the deaf and hard-of-hearing community and the hearing population
remains a critical challenge in today’s society. Traditional solutions such as interpreters or
written notes are often limited, costly, or unavailable in real-time situations, creating barriers in
education, healthcare, workplaces, and everyday interactions. Current sign language recognition
systems face several drawbacks, including low accessibility, as professional interpreters are not
always available, especially in rural or emergency contexts; limited technological solutions, as
many systems either recognize only a small set of static gestures or rely on expensive hardware
like sensor gloves; and inconsistent recognition accuracy due to variations in signing speed,
hand orientation, lighting conditions, and regional dialects.
Additionally, many existing solutions fail to incorporate non-manual cues such as facial
expressions and body posture, which are vital for accurate interpretation, while others suffer
from high latency, making them unsuitable for real-time communication. The diversity of sign
languages across different regions further complicates system design, as most solutions lack
adaptability to multiple languages. Moreover, integration challenges persist since very few
systems translate sign language into both text and speech simultaneously, limiting
communication with individuals unfamiliar with signing. These issues emphasize the need for an
efficient, accurate, and scalable Sign Language Recognition system that can provide real-time
translation, enhance inclusivity, and bridge the communication gap between hearing and non-
hearing communities.

3. Existing System
Existing systems in Sign Language Recognition (SLR) consist of a variety of technologies,
tools, and models that aim to bridge the communication gap between the deaf and hearing
communities. These systems leverage different approaches such as vision-based recognition,
sensor-based tracking, and artificial intelligence for interpreting gestures. Below are some key
components and commonly used systems:
3.1. Vision-Based Recognition Systems
Functionality: Use cameras and computer vision (CNNs, deep learning) to detect
hand shapes and movements. Accuracy depends on lighting and background.
Examples: OpenPose, MediaPipe, DeepASL.
.

1
 3.2. Sensor-Based Systems
Functionality: Use wearable devices like gloves or motion sensors to track finger and hand
movement precisely, but are costly and less practical.
Examples: CyberGlove, Leap Motion.

3.3. Hybrid Systems

Functionality:. CRM systems store guest information, preferences, and feedback,
allowing hotels to personalize services and improve guest engagement.
Examples: Guestline, Salesforce, Hotelogix.
3.4. Mobile Applications
Functionality: Smartphoneappsusecamerasand AI for translation but arelimited to
smallvocabularies and basic signs.
Examples: Hand Talk, ProDeaf, KinTrans.
3.5. Real-Time Systems
Functionality: Translate signs instantly into text/speech for live communication but face
speed and regional language challenges.
Examples: SignAll, DeepASL.
3.6. Dataset-Based Systems
Functionality: Many research projects build recognition systems trained on public
datasets of sign language images/videos. They provide good accuracy in labs but
struggle in real-world usage.
Examples: RWTH-PHOENIX-Weather 2014T, ASLLVD.
3.7. Educational Tools
Functionality: Functionality: Some systems are designed mainly for teaching and learning
sign language. They recognize basic alphabets or gestures but are not suitable for full
conversations.
Examples: ASL Alphabet Translator apps, Learn&Sign platforms.

4. Proposed System
The proposed Sign Language Recognition (SLR) system aims to integrate computer vision and
deep learning techniques to create an efficient, real-time translation tool for sign language
communication. This system will address the limitations of existing approaches by improving
accuracy, accessibility, and usability. The proposed model focuses on recognizing gestures and
converting them into meaningful text or speech, making communication seamless between the
deaf and hearing communities. Below are the key components and features of the proposed SLR
system:

4.3. Enhanced Data Collection and Preprocessing :

High-quality datasets are crucial for accurate recognition. The system will
collect sign language images and video sequences covering alphabets, words,
and phrases..
4.3.1. Centralized Dataset: Develop a dataset by integrating multiple
sign language datasets (e.g., ASL, ISL, RWTH-PHOENIX)
along with newly recorded samples to cover a wide vocabulary..
2
 4.3.2. Data Acquisition: Use webcams or mobile cameras to capture
gesture sequences, ensuring scalability and accessibility.

4.3.3. Preprocessing Techniques:

Apply image preprocessing methods such as hand segmentation,
background removal, grayscale conversion, and normalization to
standardize input.
4.3.4. Feature Selection:
Feature selection involves identifying the most relevant variables
from the gesture data that contribute to the performance of
predictive models. In the context of a sign language
recognition system, the following features can be considered
for selection.
Hand shape, finger positions, and orientation provide key
information for recognizing static gestures like alphabets and
numbers.

4.3.Model Selection :

Choosing the right model is critical for achieving high recognition accuracy.
 Static Gesture Models:
CNN-based architectures (VGG16, ResNet, MobileNet) for
recognizing alphabets and numbers.
• Dynamic Gesture Models:
Hybrid CNN-LSTM models for sequence recognition of words and
sentences
• Comparative Evaluation::
Test multiple models and select the one with the best performance across
accuracy, precision, recall, and F1-score.

4.4. Model Training and Evaluation:

 Data Splitting: The dataset will be divided into training, validation,
and testing sets (e.g., 70-20-10 split) to ensure balanced evaluation and
avoid bias. Choose relevant features based on the problem you are
trying to solve (e.g., predicting guest cancellations, forecasting
demand).
 Training: Deep learning models such as CNNs and CNN-LSTM
hybrids will be trained on labeled gesture data using supervised
learning approaches.
 Hyperparameter Tuning:
Parameters like learning rate, batch size, dropout rate, and optimizer
type will be fine-tuned using grid search or random search to
achieve optimal performance.

3
 Cross-Validation:
• K-fold cross-validation will be used to evaluate model
generalization across different subsets and prevent overfitting.
Performance Metrics:
• Accuracy, precision, recall, and F1-score will be calculated to assess
the classification results. Additionally, confusion matrices will be
used for error analysis.unseen data.
4.5. User Interface and Visualization:

4.5.1. User -Centric Design:

Provide a simple and accessible interface where users can perform
gestures in front of a camera and receive immediate text or speech output.
4.5.2. Navigation and Layout:
Ensure clear navigation and consistent layouts for usability across web and
mobile platforms.
Maintain a consistent layout across all screens to provide a familiar
experience, using grid systems to align elements properly.

5 . Algorithms
5.1. Gesture Recognition Algorithms

 Convolutional Neural Networks (CNNs): Used for extracting

spatial features like hand shape, orientation, and finger positions
from images. CNNs excel at recognizing static gestures such as
alphabets and numbers.

 Recurrent Neural Networks (RNNs) with LSTM/GRU: Useful

for dynamic gestures involving movement over time (e.g., words
or sentences). These networks capture temporal dependencies in
gesture sequences.

5.2. Feature Extraction Algorithms

 Keypoint Detection Algorithms: Frameworks like MediaPipe and

OpenPose extract hand landmarks (e.g., finger joints, palm
positions) for precise feature representation.

 Dimensionality Reduction Techniques: Methods such as PCA

(Principal Component Analysis) or Autoencoders help reduce noise
and computational complexity while retaining essential features.

 These algorithms aim to maximize revenue by analyzing booking

patterns and adjusting pricing strategies. Techniques like time
series analysis and historical data analysis can help identify peak
seasons and optimal pricing strategies.

4
 5.3. Classification Algorithms

 Softmax Classifier: Commonly used in CNN-based models

for classifying gestures into predefined categories (alphabets,
numbers, words).

 Support Vector Machines (SVMs): Can be applied on

extracted features for smaller datasets where deep
learning may be too resource-heavy.

5.4. Natural Language Processing (NLP) Algorithms

 Sequence-to-Sequence Models: Used to convert

recognized gestures into grammatically correct sentences.

 Text-to-Speech (TTS) Algorithms: Transform recognized

text into audible speech for real-time communication.

 Techniques like text classification and sentiment scoring can

be applied.

5.5. Real-Time Optimization Algorithms

 Frame Skipping and Smoothing Algorithms: Reduce

latency by processing
onlyessentialframeswhilesmoothingpredictionsfor
continuousgestures.

 Ensemble Learning: Combining multiple models (e.g., CNN

+ LSTM) to improve recognition accuracy and robustness in
real-world conditions.

6 .Approach
The approach to developing a Sign Language Recognition (SLR) system is a structured process that
integrates deep learning, computer vision, and natural language processing to ensure the system
is accurate, efficient, and user-friendly. Each stage focuses on building a solution that can
effectively interpret sign language gestures and translate them into text or speech for real- world
communication.
1.Requirements Gathering
The first step is to identify the needs of stakeholders, including the deaf and hard-of-hearing
community, educators, and researchers. This involves understanding the scope of sign
language coverage (alphabets, numbers, words, or sentences) and the expected outputs (text,
audio, or both). User interviews, surveys, and case studies help clarify practical
requirements such as real-time translation, accuracy, and support for regional sign
languages. Use cases such as classroom learning, day-to-day communication, and
accessibility support are documented to define system objectives.

5
1. System Design
System design focuses on creating a blueprint for the SLR architecture. This includes the vision-
based input module (camera feed), preprocessing pipeline, deep learning models, and output
modules for text and speech. The design also considers real-time constraints, making sure the
system can process gestures with minimal delay. Database design involves storing gesture
datasets, trained models, and user interaction logs. Mockups and flow diagrams are prepared to
visualize how gestures will be captured, processed, and translated for end-users.
2. Technology Selection
The choice of technology is critical for robust performance. Python is selected as the primary
programming language due to its strong libraries for deep learning and computer vision.
Frameworks such as TensorFlow or PyTorch are used for model development, while OpenCV and
MediaPipe handle gesture detection and preprocessing. For natural language processing and
speech conversion, libraries like NLTK and gTTS (Google Text-to-Speech) are used. For
deployment, Flask or Django may be used to build a web interface, and mobile compatibility is
considered using lightweight models like TensorFlow Lite.
3. Development Methodology
An Agile methodology is adopted to allow iterative development and continuous improvement.
The team works in sprints, with each sprint focusing on components like dataset preparation, CNN
model building, real-time integration, and UI design. Version control systems such as Git ensure
collaboration and efficient code management. Regular testing and feedback loops with potential
users guide refinements in usability and performance.
4. Implementation
In this phase, gesture datasets are collected and preprocessed to normalize image sizes, enhance
contrast, and detect keypoints. Models like CNNs and CNN-LSTM hybrids are implemented and
trained on labeled datasets to recognize both static and dynamic gestures. Integration ensures that
recognized gestures are mapped to corresponding text, and optional TTS modules generate spoken
output. Real-time processing modules are fine-tuned for low latency.
5. Testing

Testing ensures the system meets accuracy and usability requirements. Unit testing validates
preprocessing and classification modules, while integration testing checks the interaction between
camera input, model inference, and text-to-speech output. User Acceptance Testing (UAT) with
sign language users verifies the system’s practicality in real-world scenarios.
Performance is measured in terms of accuracy, latency, and robustness under varying lighting and
background condition

6
6. Deployment
After successful testing, the system is deployed in a real-time environment. Cloud platforms such
as AWS or Google Cloud can be used for scalability, while lightweight versions are deployed on
mobile or embedded devices for portability. Continuous monitoring ensures model performance,
and updates are made to expand gesture vocabulary or improve accuracy. Deployment pipelines
(CI/CD) streamline updates and integration of new features.

7. Code
import random
class
SignLanguageRecognition:
def init (self):
self.dataset = []
self.labels = []
self.trained =
False self.gestures
={
"A": "Letter A",
"B": "Letter B”,
7
 "C": "Letter C",
"Hello": "Greeting",
"Thanks":
"Gratitude"
}
def load_data(self, data, labels):
self.dataset =
data self.labels
= labels
print("Dataset loaded with", len(self.dataset), "samples.")

def preprocess(self, sample):

print(f"Preprocessing
sample:
{sample}") return sample
def
train_model(se
lf): if not
self.dataset:
print("No dataset found. Please load data first.")
return
print("Training model with", len(self.dataset), "samples...")
self.trained = True
print("Model trained successfully.")

def predict(self, sample):

if not self.trained:
print("Model not trained yet. Training now...")
self.train_model()
processed = self.preprocess(sample)
prediction =
random.choice(list(self.gestures.keys())) return
prediction

def translate(self, sample):

result = self.predict(sample)
print(f"Predicted Gesture: {result} → Meaning: {self.gestures[result]}")

processed = self.preprocess(sample)
prediction =
random.choice(list(self.gestures.keys())) return
prediction

if name == " main ":

8
slr = SignLanguageRecognition()

9
 # Load dataset
slr.load_data(["Image1",
"Image2", "Image3"], ["A", "B",
"C"])

# Train the model

slr.train_model()

# Translate gestures
slr.translate("Input_Image_1"
)
slr.translate("Input_Image_2"
)
slr.translate("Input_Image_3"
)

8. Output

Dataset loaded with 3 samples.

Training model with 3 samples...
Model trained successfully.

Preprocessing sample: Input_Image_1

Predicted Gesture: Hello → Meaning:
Greeting

Preprocessing sample: Input_Image_2

Predicted Gesture: A → Meaning: Letter A

Preprocessing sample: Input_Image_3

Predicted Gesture: Thanks → Meaning: Gratitude

10
 9. Conclusion

The Sign Language Recognition (SLR) system developed in this project demonstrates the
effective application of deep learning and computer vision techniques for enabling inclusive
communication. By capturing hand gestures and mapping them to meaningful text or speech, the
system bridges the gap between the hearing-impaired community and the general population. This
fosters better interaction, understanding, and accessibility in everyday life.
The project highlights the practical advantages of SLR, such as reducing dependency on human
interpreters and providing an automated, scalable, and consistent solution for gesture recognition.
The implemented model shows how supervised learning techniques can classify gestures
accurately when trained on an appropriate dataset, and how preprocessing and feature extraction
are critical in improving recognition performance. With such an approach, real-time gesture-to-
text translation becomes achievable, empowering individuals to express themselves without
barriers.
One of the key takeaways from this work is the system’s adaptability. As the dataset grows and
algorithms advance, the model can be retrained and fine-tuned to recognize a wider variety of
signs, including words, sentences, and even contextual expressions. This scalability makes the
solution viable for integration into educational institutions, workplaces, healthcare facilities, and
public environments where accessibility is essential.
Additionally, the system opens the door for integration with mobile and IoT-based devices,
allowing portable and real-time sign recognition. Future enhancements, such as Natural Language
Processing (NLP), could allow the translation of sign sequences into grammatically correct
sentences, further improving communication. Cloud-based deployment could also ensure that the
technology is accessible globally with minimal hardware requirements.
Although the current prototype is simplified, it successfully demonstrates the feasibility of sign
recognition and its potential to transform communication. The system contributes not only to the
advancement of AI applications but also to social empowerment, inclusivity, and accessibility. It
underlines the fact that technology, when applied thoughtfully, can remove barriers and create
equal opportunities for everyone.
In conclusion, the Sign Language Recognition system is both a technological innovation and a
socially impactful project. By leveraging AI, it creates an ecosystem where hearing-impaired
individuals can interact freely and independently with society. With continued development,
refinement, and large-scale implementation, this project has the potential to evolve into a highly
reliable and globally accepted solution that significantly enhances communication and inclusivity
for millions of people worldwide.

11
 10. Future Enhancements

Future enhancements to a Sign Language Recognition (SLR) system can significantly improve its
accuracy, usability, and adaptability to diverse real-world contexts. Below are several key areas
where the system can be improved:
1. Artificial Intelligence and Machine Learning:
Integrating advanced deep learning architectures such as Transformer-based models and 3D
Convolutional Neural Networks can improve recognition of both static and dynamic gestures.
AI-driven continuous learning could enable the system to automatically adapt to new gestures
and regional variations of sign language over time. Incorporating Natural Language Processing
(NLP) would also allow translation of gesture sequences into grammatically correct sentences,
enhancing communication quality.
2. Mobile and Wearable Applications
Developing lightweight mobile applications and wearable device integrations can make the
system more accessible for daily use. Smartphone apps using device cameras could allow real-
time translation on the go, while AR/VR or smart glasses integration could provide immersive
interaction by overlaying gesture translations directly in the user’s field of view.
3. Integration with Assistive and Smart Technologies
The system can be enhanced by integrating with assistive tools such as voice assistants, IoT
devices, and educational platforms. For instance, recognized gestures could control smart home
devices or assist in classrooms by translating sign language into spoken audio instantly. These
integrations would expand the scope of SLR beyond basic communication to practical real-
world applications.
4. Multilingual and Cross-Sign Language Support
Future versions can support multiple sign languages (e.g., ASL, ISL, BSL) with adaptability to
regional dialects and variations. This would make the system versatile and usable in global
contexts, breaking geographical barriers and promoting inclusivity across cultures.
5. Enhanced Data Analytics and Personalization
Adding advanced data analytics could provide valuable insights into user behavior, frequently
used gestures, and error patterns. This data could help in personalizing recognition models for
individual users, improving accuracy over time. Additionally, analytics can aid researchers and
educators in understanding language usage and adoption trends.

12