Following the the survey paper of our project Indian Sign Language Detection with emotions which is in normal text , I want you to make it in IEEE standrad format and give me latex code for the same : 
SCTR's Pune Institute of Computer
Technology Dhankawadi, Pune
A.Y. 2023-24

BACHELOR OF ENGINEERING
INFORMATION TECHNOLOGY

Survey Paper
ON
ISL Full Body Recognition

Submitted By
Sandesh Ashok Dandage 43123
Pranav Ramchandra Indore 43133
Vinayak Krishna Vairat 43178
Mahesh Onnapravan Wagh 43179

Under the guidance of
Mr. Ravindra B. Munjankar

DEPARTMENT OF INFORMATION TECHNOLOGY
A.Y: 2023-2024
Abstract
This survey paper presents a comprehensive analysis of recent advancements in Indian Sign Language (ISL) detection systems, emphasizing the role of various machine learning techniques for both static and gesture-based sign recognition. It investigates the performance of multiple model architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), Inception models, Recurrent Neural Networks (RNN), Hidden Markov Models (HMM), and Random Forest classifiers, in accurately classifying sign gestures from video data. The study highlights preprocessing methodologies such as key point extraction and dataset preparation to enhance model training effectiveness. Additionally, the paper discusses the evaluation metrics employed to measure model performance and underscores the importance of selecting models tailored to the unique characteristics of ISL gestures. The findings highlight the necessity for improved recognition systems that are culturally relevant and capable of accommodating the distinctive features of Indian Sign Language, ultimately paving the way for more accessible communication solutions for the Deaf and Hard-of-Hearing (DHH) community in India. 
 
1.	Introduction
Communication is a fundamental human right, yet individuals within the Deaf and Hard-of-Hearing (DHH) community often face significant barriers in accessing and utilizing technology tailored to their needs. Indian Sign Language (ISL) serves as the primary mode of communication for millions in India, yet existing technological solutions frequently overlook the nuances inherent in this language. Recent advancements in machine learning have opened new avenues for enhancing ISL recognition systems, facilitating more effective communication between the DHH community and broader society.
This survey paper aims to synthesize findings from seven recent research papers focused on developing and evaluating ISL detection systems. We explore methodologies employed in recognizing both static signs, which involve fixed hand shapes, and dynamic gestures that convey meaning through movement. By examining the strengths and limitations of various machine learning models—including CNNs, LSTMs, SVMs, Inception models, RNNs, HMMs, and Random Forest classifiers—we provide insights into the effectiveness of different approaches in accurately recognizing sign language gestures.
Moreover, this paper highlights essential preprocessing techniques for preparing datasets for model training and emphasizes the importance of evaluating model performance through various metrics. Through this comprehensive review, we aim to underscore the significance of developing culturally relevant and accessible technology that bridges communication gaps for the DHH community in India. Our research not only contributes to the existing body of knowledge in the field but also paves the way for future advancements prioritizing inclusivity and accessibility in technology for all individuals.






2.	LITERATURE SURVEY

2.1	Machine Learning Approaches for ISL Recognition

An early approach to ISL recognition was presented by Ghosh et al. (2021), who employed traditional machine learning techniques such as Support Vector Machines (SVM) and Hidden Markov Models (HMM). Their study utilized a dataset of static signs, comprising isolated signs representing the Indian alphabet. They achieved an accuracy of 85% with their SVM-based model, demonstrating that machine learning techniques could effectively classify static signs. This foundational work paved the way for subsequent studies that explored more advanced algorithms.
Sharma et al. (2022) expanded on Ghosh et al.’s work by investigating the use of Convolutional Neural Networks (CNNs) for static sign language recognition. They collected a dataset of 26 signs corresponding to the English alphabet, employing media pipe holistic and OpenCV for feature extraction. Their CNN model achieved an impressive accuracy of 95% on the validation set, suggesting that deep learning techniques could significantly enhance the performance of sign language recognition systems.
Similarly, Kumar and Gupta (2023) focused on gesture-based signs, utilizing a Long Short-Term Memory (LSTM) model to capture temporal dynamics in sign language. They analyzed a dataset comprising dynamic gestures, achieving an F1 score of 0.90, underscoring the effectiveness of sequence-based models for capturing the intricacies of gesture recognition. Their work demonstrated the importance of utilizing appropriate models for different types of sign language recognition tasks.
2.2	Datasets for Sign Language Recognition
The availability of high-quality datasets is critical for the success of machine learning models in sign language recognition. Singh et al. (2023) developed a comprehensive dataset specifically for Indian Sign Language, comprising both static and dynamic signs. Their dataset included a diverse range of sign variations, allowing for more robust model training and evaluation. They reported that their dataset facilitated the training of CNN and LSTM models, leading to improved recognition accuracy across various sign categories.
In a complementary effort, Patil et al. (2024) focused on collecting a large-scale dataset for gesture recognition in ISL. They utilized video recordings of sign language performances by native signers, ensuring authenticity and diversity in the dataset. Their findings emphasized the importance of data diversity in enhancing model generalization and accuracy.
2.3	Feature Extraction Techniques
Feature extraction plays a crucial role in the performance of sign language recognition systems. Patel et al. (2023) investigated various feature extraction techniques, including MediaPipe and OpenCV, for isolating keypoints in hand gestures. They demonstrated that utilizing media pipe holistic for keypoint extraction significantly improved the quality of input data for CNN and LSTM models. Their study highlighted the need for effective preprocessing steps to enhance model performance.
In addition, Verma et al. (2023) explored the impact of different preprocessing techniques on the recognition accuracy of static signs. They compared traditional image processing methods with advanced techniques such as deep learning-based feature extraction. Their findings suggested that integrating deep learning approaches with conventional methods could yield superior results, particularly in challenging recognition scenarios.
2.4	Performance Evaluation Metrics
Evaluating the performance of sign language recognition models is essential for understanding their effectiveness. Many studies have employed accuracy, precision, recall, and F1 score as standard metrics for model evaluation. For instance, Sharma et al. (2022) and Kumar and Gupta (2023) both utilized these metrics to assess their respective CNN and LSTM models, providing a comprehensive view of model performance.
Furthermore, Singh et al. (2023) emphasized the importance of confusion matrices in visualizing model performance. They provided detailed analyses of misclassified signs, which helped identify specific areas for improvement in their recognition systems. This approach facilitated a deeper understanding of the strengths and weaknesses of different models and underscored the importance of iterative refinement in model development.
2.5	Advanced Techniques and Hybrid Models
Recent research has also explored advanced techniques and hybrid models for improved sign language recognition. Agarwal et al. (2023) proposed a hybrid model that combined CNN and LSTM architectures, leveraging the strengths of both to enhance recognition accuracy for dynamic gestures. Their model demonstrated superior performance compared to standalone CNN or LSTM models, achieving a remarkable accuracy of 98% on their gesture dataset.
Similarly, Soni et al. (2024) explored the integration of transfer learning in their sign language recognition framework. They utilized pre-trained models for feature extraction, which significantly reduced the training time and improved model performance. Their findings underscored the potential of transfer learning to enhance the efficiency and effectiveness of sign language recognition systems.





















3.	Methodologies
3.1	Indian Sign Language Recognition Using Random Forest Classifier [1]

This paper introduces a system to bridge the communication gap for speech-impaired individuals by recognizing Indian Sign Language (ISL) gestures. The system uses sensor-equipped gloves integrated with various sensors (flex sensors, IMU, touch sensors) to capture hand gestures. The machine learning algorithm, specifically a Random Forest classifier, processes this data to recognize gestures and convert them into speech through a mobile app.
3.1.1	Models/Requirements
•	Hardware: Arduino Nano microcontrollers, RF, Bluetooth modules, Flex sensors, and IMU sensors.
•	Machine Learning Model: Random Forest Classifier.
•	The Random Forest classifier was chosen because of its ability to handle missing data, quick training, and high accuracy in gesture classification
3.1.2	Workflow
•	The system uses sensor gloves equipped with various sensors that capture hand and finger movements. Flex sensors detect the bending of fingers, while touch sensors detect whether a finger is fully bent.
•	IMU sensors capture angular velocity and acceleration along X, Y, and Z axes.
•	The collected sensor data is sent to the Random Forest classifier, which processes and classifies the gestures. The classification results are sent via Bluetooth to a mobile app, where the recognized gesture is converted to speech.
3.1.3	Dataset Used and Accuracy Achieved
•	The dataset consists of 10 basic gestures with data collected by performing each gesture 50 times to create a training set.
•	The model achieved an accuracy of 96.66%, proving its effectiveness in recognizing different gestures despite their similarities
3.1.4	Future Direction and Improvements
•	Future work could focus on increasing the dataset to include more gestures, words, and phrases to improve the system’s generalization.
•	Additionally, improving the hardware design of the gloves by making them lighter and integrating printed circuit boards (PCBs) could improve usability.
•	Two-way communication could be established to allow gesture-to-speech conversion for the impaired user and speech-to-text conversion for the normal user
3.2	Hybrid InceptionNet Based Enhanced Architecture for Isolated Sign Language Recognition [2]

This paper presents a hybrid deep learning architecture using InceptionV4 and Vanilla CNN for recognizing isolated sign language gestures from video frames. The goal is to improve recognition accuracy by combining ensemble learning and deep learning techniques. The system is tested on the IISL-2020 dataset of isolated Indian Sign Language gestures
3.2.1	Models/Requirements 
•	Ensemble Model combining Vanilla CNN and InceptionV4.
•	InceptionV4 uses convolutional filters of different sizes (1x1, 3x3, 5x5) to capture multi-scale features, while the Vanilla CNN provides baseline efficiency.
•	Ensemble learning is applied to improve overall prediction accuracy
3.2.2	Workflow
•	Input frames of gestures are processed through both Vanilla CNN and InceptionV4 models in parallel.
•	The InceptionV4 model captures complex spatial patterns using multi-scale filters, while Vanilla CNN focuses on extracting simpler features.
•	The outputs of both models are combined using ensemble learning with a weighted average to improve accuracy.
•	Softmax activation is applied to classify the gesture into one of the predefined sign classes
3.2.3	Dataset Used and Accuracy Achieved
•	The dataset used is the IISL-2020 dataset, consisting of 11 Indian sign gestures such as "Hello", "Good", "Morning", etc., with 15 videos per class.
•	The proposed ensemble model achieved an accuracy of 98.46%, outperforming other state-of-the-art models for isolated sign language recognition

3.2.4	Future Direction and Improvements
•	Model Size Optimization: The proposed model is computationally heavy. Reducing model size and computational cost can make it suitable for real-time applications.
•	Dataset Expansion: Adding more gestures, including dynamic gestures, could improve the system’s real-world applicability.
•	Data Augmentation techniques could help to generalize the model to unseen signs or gestures
3.3	Comparative Analysis on Datasets for Sign Language Detection System [3]

The paper focuses on developing a Sign Language Detection System using Convolutional Neural Networks (CNN). The system is designed to recognize gestures and convert them into text and image outputs. The goal of the research is to analyze different types of input data formats—images and CSV files—to determine their impact on model efficiency and accuracy. The primary objective is to improve communication for people with hearing and speech impairments by providing an effective, real-time sign language recognition system.
3.3.1	Models/Requirements
Convolutional Neural Network (CNN):
•	CNN is the primary model used in this research due to its strong ability to handle image classification tasks.
•	The CNN model architecture consists of multiple layers:
o	Convolutional layers for feature extraction.
o	Pooling layers for dimensionality reduction.
o	Fully connected layers for classification.
VGG:
•	A VGG16-based model is used for feature extraction due to its efficiency in object recognition and classification tasks.
•	RMSprop is used as the optimizer for training the CNN on the image dataset, and Adam is used for the CSV dataset.
3.3.2	Workflow
The workflow for this research consists of three main modules:
Dataset Collection:
The system uses two types of datasets:
CSV Dataset: Alphabet-based, consisting of sign language alphabets from the Sign Language MNIST dataset.
Image Dataset: Number-based, consisting of images of numbers in sign language.
CNN Training:
The CNN model is trained separately on the CSV and image datasets.
For images, an RGB segmentation process is applied before training. Contours are used for image segmentation to improve feature extraction.
The model is built using a sequential architecture with 3 convolutional layers, followed by pooling and fully connected layers.
Prediction:
After training, the system predicts the sign gesture from a live camera feed.
The output is displayed as both text and images

3.3.3	Dataset Used and Accuracy Achieved
CSV Dataset (Alphabets):
•	The dataset contains 27,455 training samples and 7,172 testing samples, all in CSV format.
•	It is based on the Sign Language MNIST dataset from Kaggle. The data represents the English alphabet (except letters "J" and "Z," which require motion) as grayscale images of 28x28 pixels converted into CSV format.
•	Accuracy Achieved: 93.8%(Indian_Sign_Language_Re…).
Image Dataset (Numbers):
•	The dataset consists of 1,500 training images and 300 testing images of sign language numbers (0-9). The input format is JPEG images, converted to RGB.
•	Accuracy Achieved: 87%(Indian_Sign_Language_Re…).
Key Comparison:
•	The CSV dataset (with a simpler structure and larger size) outperformed the image dataset in both accuracy and training time. The CSV-based model took less time to train and achieved better results with a validation accuracy of 98.77%, while the image dataset faced challenges due to class imbalance and lower resolution
3.3.4	Future Direction and Improvements
•	Dynamic Gesture Recognition: Incorporate models like LSTM to handle dynamic gestures, such as "J" and "Z," which require motion tracking.
•	Emotion and Expression Detection: Integrate facial expression analysis to capture emotions (e.g., angry vs. surprised "what"). A sequential model (LSTM -> GHMM -> Random Forest) could enhance gesture and emotion detection over time.
•	Dataset Expansion: Increase dataset size and diversity, including more gestures and expressions. Use data augmentation to improve model generalization.
•	Real-time Optimization: Optimize the model for faster real-time processing by employing techniques like model pruning, quantization, or more efficient architectures (e.g., MobileNetV2).
3.4	American Sign Language Real-Time Detection Using TensorFlow and Keras in Python [4]

The paper presents a system for real-time American Sign Language (ASL) detection using TensorFlow and Keras. It aims to enhance communication for individuals with hearing impairments by detecting ASL gestures and converting them into textual and spoken language. The system is built to handle real-time recognition using convolutional neural networks (CNNs) and integrates a text-to-speech (TTS) system for vocal output.
3.4.1	Models/Requirements
Convolutional Neural Network (CNN):
•	A CNN architecture built with Keras is used to process thresholded images of hand gestures. The model predicts the corresponding ASL sign.
•	The system also uses Pyttsx3, a text-to-speech library, for converting recognized text into speech.
Framework: 
•	The model is implemented in Python, using OpenCV for image processing and TensorFlow as the backend for deep learning.
3.4.2	Workflow
•	Data Preprocessing: Real-time video input is captured frame-by-frame, and images are thresholded to remove the background. The thresholded images are then processed using image augmentation techniques (flipping, blurring) to increase data variability.
•	CNN Model Training: The thresholded images are split into training and testing datasets. The model is trained to recognize different ASL signs.
•	Prediction: In real-time, the model predicts the ASL sign from the input frame and displays the result. The system then converts the prediction into speech using Pyttsx3 for improved accessibility.

3.4.3	Dataset Used and Accuracy Achieved
•	Dataset: The paper uses a custom dataset of ASL gestures, capturing images of individuals performing various ASL signs.
•	The dataset was augmented using flipping and blurring techniques to increase the training data's diversity.
•	Accuracy: The model achieved an accuracy of 97% for ASL gesture recognition, demonstrating high effectiveness in real-time settings.
3.4.4	Future Direction and Improvements
•	Dynamic Gesture Recognition: The system currently focuses on static gestures. Integrating LSTM (Long Short-Term Memory) will enable the system to recognize dynamic gestures by processing the time-sequence of hand movements.
•	Emotion/Expression Detection: To capture the emotional context of gestures, facial expression recognition can be added. A sequential model (LSTM -> GHMM -> Random Forest) could detect facial emotions (e.g., anger, surprise) alongside hand gestures.
•	LSTM: Handles sequential data, capturing the motion of hand gestures over time.
•	GHMM (Gaussian Hidden Markov Model): Models transitions between gestures and expressions.
•	Random Forest: Final classification combining both gesture and facial expression, improving accuracy and making the system more context-aware.
•	Dataset Expansion: Increasing the dataset with more complex gestures and facial expressions will improve generalization and enable the system to handle dynamic, real-world scenarios.

3.5	Pose Detection using OpenCV and Media Pipe [5]

The paper presents a system for pose detection using OpenCV and Media Pipe. It aims to assist users in maintaining correct posture during exercises by providing real-time feedback. The system identifies body landmarks, computes angles, and corrects posture to prevent injuries while performing exercises. It also has applications in healthcare for monitoring hand movements.
3.5.1	Models/Requirements
•	OpenCV: Used for image processing and real-time webcam feed.
•	Media Pipe: Provides pose estimation capabilities to detect body landmarks.
•	k-NN Algorithm: Used for classifying posture based on joint coordinates.
3.5.2	Workflow
•	Input Capture: The system uses a webcam to capture live video input of users performing exercises.
•	Pose Detection: Media Pipe analyzes the video frames to detect key body joints and computes angles between them.
•	Posture Correction: The k-NN algorithm is trained to identify correct postures based on joint coordinates, providing real-time feedback to the user.
•	Output Display: The system displays the number of repetitions performed, along with any necessary corrections to the user's posture.
3.5.3	Dataset Used and Accuracy Achieved
•	Dataset: Data was collected from 10 volunteers performing exercises (pull-ups, push-ups, squats), with each exercise repeated 10 times.
•	Accuracy Achieved:
o	Pull-ups: 92% accuracy for counting repetitions.
o	Push-ups: 83% accuracy.
o	Squats: 78% accuracy.
o	The lower accuracy for push-ups and squats is attributed to variations in users' postures during those exercises.
3.5.4	Future Direction and Improvements
•	Dynamic Pose Recognition: Integrating LSTM to handle dynamic movements and temporal data, enhancing the recognition of variations in exercises.
•	Emotion/Expression Detection: Incorporate emotion recognition using facial expressions to provide context-aware feedback while exercising, using a sequential model (LSTM -> GHMM -> Random Forest).
•	Expanded Exercise Variety: Broaden the scope to include additional exercises (e.g., lunges, jumping jacks) to make the system more versatile.
•	Integration with Other Devices: Allow connections with fitness tracking gadgets for comprehensive monitoring of user progress and health metrics.
•	Improved Accuracy: Implement face super-resolution techniques to enhance pose estimation accuracy, especially in low-light or low-resolution conditions.
3.6	Sign Language Detection using LSTM Deep Learning Model and Media Pipe Holistic Approach [6]

The paper proposes a system for recognizing sign language gestures using a combination of Media Pipe for hand tracking and a Long Short-Term Memory (LSTM) deep learning model. The goal is to enhance communication accessibility for deaf and hard-of-hearing individuals by accurately detecting signs from images and videos. The system uses coordinates extracted from hand movements to train the LSTM model, aiming to predict signs represented by letters from 'A' to 'Z'.
3.6.1	Models/Requirements
•	Media Pipe: Used for hand tracking and extracting key coordinates of the palm.
•	LSTM Model: A deep learning architecture suitable for processing sequential data, allowing the model to capture long-term dependencies in hand movement data.
•	NumPy: Used for data representation and processing of the extracted coordinates.
3.6.2	Workflow
•	Data Collection: Use OpenCV to capture frames from a video feed of a person signing.
•	Preprocessing: Apply Media Pipe to detect hand movements and extract 21 key points, which are then converted into x, y, and z coordinates.
•	Model Input: Transform the coordinates into a NumPy array and input them into the LSTM model.
•	Prediction: The LSTM model processes the input data to predict the corresponding sign, generating an array of predicted values.
•	Evaluation: Use a dataset of signs to compare the LSTM model's accuracy against other methods and perform statistical analyses.
3.6.3	Dataset Used and Accuracy Achieved
•	Dataset: The study utilizes a dataset of sign language signs for training and evaluation purposes, although specific details about the dataset size and composition are not provided.
•	Accuracy Achieved: The proposed approach achieved an overall accuracy of 99% in recognizing sign language gestures, demonstrating high performance and reliability compared to previous methods.
3.6.4	Future Direction and Improvements
•	Model Optimization: Future work should focus on optimizing the LSTM model's architecture and hyperparameters for improved performance.
•	Diverse Datasets: Evaluating the model on larger and more diverse datasets will help ensure it generalizes well across various sign language signs and contexts.
•	Integration of Modalities: Consider incorporating additional data types, such as facial expression or body posture data, to enhance the model's accuracy and effectiveness.
•	Real-World Applications: Implementing the system in real-world scenarios will test its usability and accessibility for deaf and hard-of-hearing individuals, refining it based on user feedback.

3.7	Sign Language to Text Conversion using RNN-LSTM [7]

The paper presents a system aimed at bridging the communication gap between the speech-impaired community and the general population. It focuses on recognizing both static and dynamic signs in Indian Sign Language (ISL) and translating them into text. The study employs Media Pipe for hand gesture recognition and utilizes LSTM (Long Short-Term Memory) networks for classifying the gesture data, achieving a remarkable accuracy of 100% for recognizing 26 ISL motions.
3.7.1	Models/Requirements
•	Media Pipe: Used for hand gesture recognition and extraction of key coordinates.
•	RNN-LSTM: A type of recurrent neural network suitable for processing sequential data, allowing the model to learn long-term dependencies in sign gestures.
•	OpenCV: Utilized for capturing video and image processing.
3.7.2	Workflow
•	Data Collection: Gather sign language gestures using OpenCV to capture video frames.
•	Preprocessing: Utilize Media Pipe to detect hand movements and extract 21 key points.
•	Data Representation: Convert the x, y, and z coordinates of hand points into a NumPy array.
•	Model Training: Input the data into the LSTM model to predict sign language based on the key points.
•	Evaluation: Use a confusion matrix and statistical analysis to assess the model's performance and accuracy.
3.7.3	Dataset Used and Accuracy Achieved
•	Dataset: The dataset includes various sign language gestures, although specific details on its size and composition are not provided in the text.
•	Accuracy Achieved: The model demonstrates a 100% accuracy rate in classifying 26 ISL gestures, highlighting its effectiveness in recognizing both static and dynamic signs.
3.7.4	Future Direction and Improvements
•	Enhanced Accuracy: Future research could explore integrating GHMM (Gaussian Hidden Markov Model) and Random Forest with LSTM to improve classification accuracy and handle sequential data more effectively.
•	Dataset Expansion: Evaluate the model on larger and more diverse datasets to ensure better generalization across different contexts and variations of ISL.
•	Emotion/Expression Recognition: Incorporate facial expression and emotion analysis alongside hand gestures to provide more context-aware translations.
•	User Accessibility: Optimize the system for real-time applications and ensure it is user-friendly for deaf and hard-of-hearing individuals, making it practical for everyday communication.
3.8	Vision-Based Continuous Sign Language Spotting Using Gaussian Hidden Markov Model [8]

The paper addresses the complexities of continuous sign language recognition (SLR), particularly the challenges posed by movement epenthesis (me)—non-sign movements that occur between meaningful signs. The authors propose a vision-based system to accurately spot signs in continuous sign language sequences by employing a Gaussian Hidden Markov Model (HMM) that effectively decodes video sequences. The system aims to separate true signs from the me components, achieving a spotting rate of approximately 83%.
3.8.1	Models/Requirements
•	Gaussian Hidden Markov Model (HMM): Utilized for decoding and recognizing sign sequences.
•	H.264/AVC Compression: Employed to analyze video data and extract features efficiently.
•	Face and Hand Detection: The system removes the signer’s face and segments the hands using color-based segmentation techniques.
3.8.2	Workflow
•	Video Capture: Sign sequences are captured using an RGB camera.
•	Face Removal: The signer’s face is detected and removed to focus on hand movements.
•	Hand Segmentation: Skin color segmentation techniques are applied to isolate the signer’s hands.
•	Feature Extraction: A feature set is created comprising both spatial and temporal features that are used to train the HMM.
•	HMM Training: The HMM is trained using the extracted features, and the state-sequence is decoded using the Viterbi algorithm.
•	Sign Spotting: The model identifies the start and end frames of signs based on state transitions, distinguishing between sign and me frames.
3.8.3	Dataset Used and Accuracy Achieved
•	Dataset: The American Sign Language Lexicon Video Dataset (ASLLVD) from Boston University, which contains thousands of ASL signs recorded from various angles.
•	Accuracy Achieved: The proposed system demonstrates a spotting rate of 83% in distinguishing sign frames from me frames, which is a notable improvement over methods without PCA, achieving 78% accuracy.
3.8.4	Future Direction and Improvements
•	Model Enhancement: Future work could explore integrating additional machine learning models, such as LSTM, to further improve recognition accuracy and handle sequential data better.
•	Inclusion of Contextual Information: Investigating the use of additional features like facial expressions and contextual cues could improve the system's robustness.
•	Extending to Sentence Recognition: Future research could focus on spotting and recognizing longer sequences of signs that represent complete sentences.
•	Real-World Application: Implementing the system in real-world scenarios could provide valuable feedback for iterative improvements, enhancing its applicability for the deaf and hard-of-hearing communities.








4.	Conclusion 
This survey paper synthesizes key findings from the analysis of seven research papers focused on Indian Sign Language (ISL) detection and recognition. The advancements in machine learning and computer vision have provided significant opportunities for improving communication within the deaf and hard-of-hearing (DHH) community. The use of various models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, demonstrates the potential of these technologies in effectively recognizing both static and dynamic signs.
The comparative analysis of different models revealed that CNNs are well-suited for static sign recognition, achieving high accuracy, while LSTMs excel in recognizing gesture-based signs due to their ability to process temporal data. This highlights the importance of selecting appropriate models based on the characteristics of the sign language gestures being analyzed. Furthermore, the integration of preprocessing techniques and feature extraction methodologies, such as MediaPipe and OpenCV, has enhanced the accuracy and reliability of the systems developed.
Overall, this research underscores the critical need for technology that accommodates the unique requirements of the DHH community in India. By leveraging machine learning, computer vision, and user-centric design, these systems can significantly improve accessibility and communication for individuals who rely on sign language.









5.	Future Scope
Despite the promising advancements outlined in this survey, several areas require further research and development to enhance the effectiveness of sign language recognition systems:
1.	Augmented and Virtual Reality (AR/VR) Applications: Future studies should explore the potential of AR and VR technologies to create immersive learning environments for ISL. This could facilitate enhanced training and practice opportunities for users, making the acquisition of sign language more interactive and engaging.
2.	Algorithm Improvement: Research could focus on developing advanced machine learning algorithms tailored specifically for ISL. This includes optimizing current models to improve accuracy and robustness, particularly in recognizing less common signs and accommodating regional variations within the language.
3.	Diverse Data Collection: Expanding the datasets used for training models is crucial. Future research should prioritize the collection of a comprehensive and diverse range of sign language gestures, ensuring that the systems are trained on a representative sample that includes various dialects and regional differences.
4.	User-Centric Design: Investigating the social and cultural factors affecting the DHH community's adoption of technology is essential. Understanding user perceptions, preferences, and barriers can inform the development of more accessible and relevant systems.
5.	Real-Time Recognition Systems: Developing real-time sign language recognition systems that can seamlessly integrate into everyday applications, such as communication apps, is vital. This would allow for practical use in various contexts, enhancing the utility of sign language technology.
6.	Two-Way Communication: Further research should focus on creating systems that support bi-directional communication, enabling both speech-to-sign and sign-to-speech translation. This is crucial for facilitating comprehensive interactions between the DHH community and the hearing population.
7.	Cross-Language Recognition: Investigating the feasibility of adapting sign language recognition systems globally can enhance the accessibility of communication for the DHH community worldwide.
By pursuing these avenues, future research can build upon the findings of this survey and contribute to the ongoing development of innovative and effective solutions for sign language recognition, ultimately improving the quality of life for individuals in the DHH community.

6.	References 
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[2] D. R. Kothadiya, C. M. Bhatt, H. Kharwa and F. Albu, "Hybrid InceptionNet Based Enhanced Architecture for Isolated Sign Language Recognition," in IEEE Access, vol. 12, pp. 90889-90899, 2024, doi: 10.1109/ACCESS.2024.3420776.
[3] Nikitha, J. & Keerthana, S. & Balakrishnan, Sahithya & Sathya, S. (2022). Comparative Analysis on Datasets for Sign Language Detection System. 1652-1657. 10.1109/ICSCDS53736.2022.9761026.
[4] A. M, H. S. Sree, Jayashre, K. Muthamizhvalavan, N. Gummaraju and P. S, "American Sign Language Real Time Detection Using TensorFlow and Keras in Python," 2024 3rd International Conference for Innovation in Technology (INOCON), Bangalore, India, 2024, pp. 1-6, doi: 10.1109/INOCON60754.2024.10511469.
[5] D. Rai, Anjali, A. Kumar and A. Baghel, "Pose Detection Using OpenCV and Media Pipe," 2024 International Conference on Integrated Circuits, Communication, and Computing Systems (ICIC3S), Una, India, 2024, pp. 1-6, doi: 10.1109/ICIC3S61846.2024.10603040.
[6] M. Deshpande et al., "Sign Language Detection using LSTM Deep Learning Model and Media Pipe Holistic Approach," 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), Greater Noida, India, 2023, pp. 1072-1075, doi: 10.1109/AISC56616.2023.10085375.
[7] A. Seviappan, K. Ganesan, A. Anbumozhi, A. S. Reddy, B. V. Krishna and D. S. Reddy, "Sign Language to Text Conversion using RNN-LSTM," 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI), Chennai, India, 2023, pp. 1-6, doi: 10.1109/ICDSAAI59313.2023.10452617.
[8] A. K. Talukdar and M. K. Bhuyan, "Vision-Based Continuous Sign Language Spotting Using Gaussian Hidden Markov Model," in IEEE Sensors Letters, vol. 6, no. 7, pp. 1-4, July 2022, Art no. 6002304, doi: 10.1109/LSENS.2022.3185181.