Sign Language Detection using machine learning

Shubam Thakyal
Model institute of engineering and
technology
2021a1r002@mietjammu.in

into corresponding text. For example, a gesture for the letter

ABSTRACT “A” in sign language can be converted into the English letter
“A” in text or speech[4][5].
Communication is important for everyone, but people with
hearing or speech impairments face challenges in
communicating with others. People who are mute or deaf rely
on visual communication, like sign language, which can be
difficult for others to understand. To make communication
easier between people with disabilities and the general public, a
system that translates hand gestures into text and speech is
needed [1].

This project focuses on creating a real-time sign language

detection system using computer vision and machine learning.
Sign language is a natural way for people to communicate, but
many people don’t know it. Our system uses deep learning
models, like Convolutional Neural Networks (CNNs), to
recognize hand gestures from American Sign Language (ASL).
The system captures hand gestures using a webcam, with
MediaPipe to detect key points and OpenCV to process the
images. The system then matches the gestures to corresponding
words in ASL [2].

We trained the system using different machine learning

algorithms, such as Random Forest, Support Vector Machines
(SVM), and k-Nearest Neighbors (KNN). The best model was
chosen based on performance measures like F1-score, precision,
and recall. The trained model recognizes gestures in real-time,
converts them into text, and then turns that text [3][4]. This
system helps break down communication barriers and makes it
easier for people with hearing and speech impairments to
communicate.

keywords: Sign language, Detection, Recognition,

Computer vision, Image classification, Performance
evaluation, Accuracy.

1. INTRODUCTION Fig. 1. Sign Language Hand Gestures

Deaf (hard of hearing) and mute individuals primarily use
In deep learning, Convolutional Neural Networks (CNNs)
Sign Language (SL) to communicate with their community
and others. SL is based on hand and body gestures and has its and LSTM are one of the most popular algorithms,
own vocabulary, meaning, and grammar, which differs from especially for image and video tasks
spoken and written languages. Spoken language relies on
sounds and words to convey messages, while sign language By using a combination of these advanced CNN and LSTM
uses visual gestures to do the same[1]. models, we can achieve an almost 100% accurate model
which will recognize the hand gestures. This model Scan be
Around 138 to 300 different types of sign language are
used globally today. In India[2]. however, there are only about deployed on web platforms on applications, or embedded
250 certified sign language interpreters for a population of devices, where it will recognize gestures in real-time using a
about 7 million deaf individuals. This shortage makes it live camera feed and convert them into text. This system
difficult to teach sign language to those who need it[3]. would help deaf and mute individuals communicate more
easily[.
Sign Language Recognition (SLR) aims to solve this
problem by recognizing hand gestures and converting them
into text or speech. With advancements in computer vision
and deep learning, it has become possible to create powerful
models that can classify these hand gestures and convert them

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE

 • Word-based signs: Specific signs that represent
particular words or meanings, and
• Non-manual features: Gestures like facial
expressions and body movements.
Developing a sign language translator is difficult because
signs may look different depending on the person signing and
the angle from which they are viewed. This project aims to
create a static sign language translator using Convolutional
Neural Networks (CNNs) and Long Sort Term
Memory(LSTM) that can work on low-resource devices like
embedded systems or web apps.

Fig. 2. Convolution Neural Networks • 1.3 Objectives

The goal of this project is to help improve automatic sign
language recognition and translation into text or speech. Our
project focuses on recognizing static hand gestures for the 26
English alphabets (A-Z) and the 10 digits (0-9) using Deep
Neural Networks (DNN).
The main objectives are:
• Media Pipe employs a pre-trained CNN to detect and
extract hand landmarks to recognize hand gestures
and match them with English alphabets and numbers.
• Training the model with LSTM and our custom
model.
• Test the model in real-time using a live camera feed.

Fig.3. Long Stort Term Memory

2. LITERATURE REVIEW
Creating a Sign Language Recognition (SLR) system Deep sign: Sign Language Detection and Recognition
offers several benefits: Using Deep Learning
• It can translate hand gestures into text or speech,
making communication easier for deaf and mute This paper explores Indian Sign Language (ISL)
people, especially in places like airports, hospitals, or recognition using deep learning models, specifically LSTM
post offices. and GRU, on the IISL2020 dataset. The proposed model
• SLR can also translate videos into text or speech, outperforms existing ISL recognition systems, especially for
helping hearing and deaf people communicate with common words like ‘hello’, ‘good morning’, and ‘work’.
each other. Adding more layers to LSTM and GRU, and combining them,
improves the model’s accuracy. Future improvements could
Problem Statement include training on better datasets, adjusting camera angles,
Sign language involves many hand and arm movements and using wearable devices. The current model focuses on
that convey meaning. Different countries have their own sign isolated signs, but future work could expand to continuous
languages and gestures, and some signs are formed by sign language recognition, potentially using vision
combining gestures for each letter of a word. transformers for even more accurate results.

Sign language has two types of gestures: Advancements in Sign Language Recognition: A
Comprehensive Review and Future Prospects
• Static gestures: These are used to represent
Deep learning models like CNNs, RNNs, and hybrid
individual letters (A-Z) and numbers (0-9).
models have shown high accuracy in sign language
• Dynamic gestures: These are used for more recognition, particularly for real-time communication
complex ideas, such as words and sentences, and between hearing and hearing-impaired individuals. CNNs
may involve hand movements, head motions, or excel at static gestures, while RNNs, such as LSTM and GRU,
both. handle sequential gestures effectively. Non-manual features,
like facial expressions and head movements, are crucial for
Sign language is a visual language and has three main improving recognition accuracy, especially in distinguishing
components: regional variations. Future systems will benefit from
• Finger-spelling: Spelling out words one letter at a comprehensive global datasets and the inclusion of additional
time, features, like lip and cheek movements, leading to more
efficient and accurate sign language recognition.
 Machine learning methods for sign language
recognition: A critical review and analysis

Advancements in machine learning and computational

intelligence have increased interest in sign language
recognition (SLR). A study of 649 articles from 2001 to 2021
shows that machine learning technologies for SLR have been
growing steadily over the past 12 years. Research has focused
on various stages of vision-based SLR, including image
acquisition, segmentation, feature extraction, and
classification. Despite the success of devices like Data glove,
Kinect, and cameras, challenges such as high costs, low
resolution, and complex backgrounds still affect accuracy.

Table 1: Analysis of Existing work on sign language detection

AUTHOR METHODOLOGY LIMITATIONS

Kshitij Bantupalli Video sequences are fed The model ends up

and into the model, which learning inaccurate
Ying Xie then extracts temporal features from the films
International and spatial information while testing with
Conference, from them. varying skin tones and
10-13 December including diverse faces,
2018[18 which causes accuracy
to decline over time.
Aju Dennisan Making a model to They have done the
Journal, 2019[7] recognize ASL alphabet recognition only on the
from RGB images. alphabets with 83.29%
accuracy.
K Amrutha Developing a system that The model showed an
and can read and interpret a accuracy of only 65%
P Prabu International sign using a dataset and the best algorithm. due to less data set.
Conference,
11-12 February 2021[5]
A. Mittal, P. Kumar, A Modified LSTM The model showed an
P. P. Roy, Model for Continuous accuracy of only 72.3%
R. Balasubramanian, Sign Language and 89.5% due training
And B.B. Chaudhuri[2] Recognition Using dataset.
Leap Motion
M. Wurangian[6] American sign language They have done the
Alphabet recognition recognition only on the
alphabets.
P. Likhar, N. K. Deep Learning This model was
Bhagat and R. G N[16] Methods for Indian developed for Indian
Sign Language Sign Language, our
Recognition model is concerned with
American Sign
Language.
Shivashankara, Ss, American sign Model is restricted to
and S. Srinath[19] Language recognition alphabet recognition
system: an optimal only.
approach
 3. METHODOLOGY
3.1 Dataset
We have used multiple datasets and trained multiple
models to achieve good accuracy.
3.1.1 ASL
Alphabet The data is a collection of images of the alphabet
from the American Sign Language, separated into 26 folders
that represent the various classes. The training dataset consists
of 520 images which are 300x360 pixels. There are 26 classes
of which 26 are English alphabets A-Z
3.1.2 Sign Language Gesture Images Dataset
Data is collected from the system’s in-built camera using
the OpenCV [8] library. A total of 30 images sequences are
captured for each gesture out of which 30 frames are extracted
from each of the photos. The environment for further
preprocessing and analysis of image data. Table 4 shows the
list of libraries necessary to provide an environment for
implementation of the sign recognition process The dataset
consists of 26 different hand sign gestures which include A-
Z alphabet gestures. Each gesture has 20 images which are
300x360 pixels, so altogether there are 29 gestures which
means there 520 images for all gestures. Fig 5 landmark Extraction
3.3 Data Pre-processing
As part of data pre-processing, captured images or frames
Table 4. List of Libraries are transformed from BGR (Blue-Green-Red) to RGB (Red-
Green-Blue). Then the converted images or frames are set to
Tensorflow and to run mathematical operations on un-writeable file mode to save memory and reduce
CPU and GPU. complexity. Data cleaning is an essential step of data
Tensorflow-gpu
preprocessing in the proposed methodology because it avoids
Opencv-python to access webcam keypoints to failed feature 26 detection in case of blurry or hazy image
extract hand landmarks frames
Mediapipe to extract hand landmarks After collecting the images, we convert them into
numerical data that a machine can understand. The data is
Scikit-learn for evaluation matrix as well as to organized into sequences, where each sequence represents a
leverage a training and testing split gesture over multiple frames.
Matplotlib to visualize images easily The steps we have taken for image Pre-processing are:
numpy to work with different arrays & ✓ Read Images.
structure different datasets to work
with file handling to store dataset ✓ Resize or reshape all the images to the same

os to work with file handling to store ✓ Remove noise.

dataset to visualize images easily
pyplot to visualize images easily 3.4 Gesture Recognition
To train a machine learning model that learns to recognize
gestures based on the extracted keypoints. To achieve this in
3.2 Hand Landmark this phase the photo data samples are taken as input to train
Here around 21 ,3-dimensional hand landmarks in the the model. So, the data collection performed in the previous
form of (x,y,z) coordinates are extracted .Hand landmarks phase commences the model. The recognition process is
consist of two types, namely the palm keypoints and finger featured by the positional keypoints of the palm and fingers
based keypoints as shown in Figure 5.This task of hand and the relative angles. Each sample constitutes a total of 30
landmark extraction is achieved by combined implementation photos .This extracted data is integrated into a npy format file.
of two supervised based learning models. One focuses on the Additionally, a set of labels are created which correspond to
palm landmarks and the other localizes keypoints with respect the classes of the data samples. For the training of the collected
to hand fingers. This is achieved by the Blaze Palm detector data, deep learning based LSTM model is used.
which is a part of the Mediapipe model. This environmental
setup reduces the time complexity of detecting the palm by
taking the focus off on unnecessary objects in the image
 Figure 6. Data collection in folders

3.5 Data Preparation and Label Creation

So after the completion of the data collection process, the
key points extracted from the data are then structured using
data preprocessing. The data is structured in such a way that
all the arrays of key points of each gesture are saved as one
numpy array (X), which is then mapped to another numpy
array of labels (Y). We then use the to_categorical function to
convert Y into a binary class matrix, such as [1,0,0....] for
hello, [0,1,0,0....] for thanks, and [0,0,1,0,0,.....] for "I love
you," and so on. Following the completion of the Figure.7 - The Architecture of the LSTM block
preprocessing, the data is split into training and testing data
using the train testsplit function.
There are 3 gates and a module called as a memory cell in
Implementation of LSTM Long short term memory a single LSTM model's architecture (see Figure 7 & Figure 8).
LSTM is an RNN variant that is primarily used for data i. Forget Gate - Information that is no longer helpful is
sequences or data in time-series. The LSTM unit is more removed from the cell state by the forget gate. When
complex than the RNN unit. It has gates that control the flow knowledge is no longer pertinent to the context, it can be
of data from a single unit. The loop is an essential component forgotten according to it.
of the LSTM architecture because it can store previous history
of information in the internal state memory. This is done to ii. Input Gate - The purpose of the input gate is to enrich
extract both spatial and temporal information from data. the cell statewith crucial information. It is concerned with
what new details can be added to or updated in our working
storage state.
iii. Output Gate - It is in charge of extracting useful
information from the current cell state and presenting it as
output. What section of the total data contained in the current
state should be provided as the output in a particular instance?
Long-term memory, which is a self-state, and short-term
memory are the two states that memory cells can be in. A
value between 0 and 1 is applied to each gate's operation. Any
Fig. 14 - The Architecture of Recurrent Neural Network. value between 0 and 1, as well as the values in between, results
in no information being transmitted.

Fig 8. The Repeating module in an LSTM

 Our information is organized as a collection of frames,
each of whichcontains details on the locations of different
landmarks that were collected. Every one of these frames is
recorded as a NumPy array. With the sequential notion, the
LSTM network is created. Three thick layers are the first of
four layers in the LSTM. "sigmoid" activation function is
applied to the first six levels. For the last dense layer,
"softmax" is employed. For stochastic gradient descent, a
optimizer called ADAM is employed. Throughout model
training, accuracy and loss for both training and validation are
recorded after each epoch.

Fig : architecture of sign language detection

 [11] Graves, A.-r. Mohamed, and G. E. Hinton. “Speech
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 REPORT
Sign Language Detection Using Machine Learning
Title: Sign Language Detection Using Machine Learning
Author: Shubam Thakyal
Institute: Model Institute of Engineering and Technology
Email: 2021a1r002@mietjammu.in
Date: December 2024

• Abstract
Communication is vital for human interaction, but individuals with hearing or
speech impairments face challenges in communicating with the general public. Sign
language serves as a bridge for communication, but it is often unfamiliar to those who
do not use it daily.
This project focuses on developing a real-time sign language detection system
using computer vision and machine learning. The system captures hand gestures via
a webcam, processes them using frameworks like MediaPipe and OpenCV, and
classifies them using deep learning algorithms such as Convolutional Neural
Networks (CNNs) and Long Short-Term Memory (LSTM) networks.
The system is trained on datasets including American Sign Language (ASL)
alphabet images and achieves high accuracy in real-time detection of static gestures
for alphabets (A-Z) and digits (0-9). This project aims to bridge the communication
gap, enabling seamless interaction for individuals with hearing and speech
impairments.
Keywords: Sign language, detection, recognition, computer vision, image
classification, deep learning, real-time systems.

• 1. Introduction
Sign language is a visual means of communication used predominantly by
individuals with hearing or speech impairments. It involves hand gestures, facial
expressions, and body movements to convey meaning. However, most people are
unfamiliar with sign language, leading to communication barriers.
The goal of this project is to develop a system capable of real-time recognition of
sign language using machine learning. By employing deep learning techniques like
CNNs and LSTMs, the system interprets static hand gestures and translates them into
text, making communication more accessible.

• 2. Problem Statement
 Sign language recognition is a challenging task due to the variation in hand
gestures, lighting conditions, and backgrounds. Existing methods often require
specialized equipment, making them inaccessible to the general public.
Challenges:
• Variations in individual signing styles.

• Recognition of both static and dynamic gestures.

• Integration into low-resource environments like embedded systems or mobile

applications.
This project addresses these challenges by using robust machine learning
techniques and readily available hardware, such as webcams.

• 3. Objectives
The primary objectives of this project are:
1. Develop a system to recognize static hand gestures for alphabets (A-Z) and
digits (0-9).
2. Utilize MediaPipe for hand landmark detection.
3. Train deep learning models (CNN and LSTM) for gesture classification.
4. Enable real-time detection using a live camera feed.
5. Evaluate the system using performance metrics such as accuracy, precision,
recall, and F1-score.

• 4. Literature Review
1. Deep Sign: Sign Language Detection and Recognition Using Deep Learning
This paper emphasizes the use of LSTM and GRU for Indian Sign Language
recognition, achieving improved accuracy through model optimization.
2. Advancements in Sign Language Recognition
CNNs and RNNs have demonstrated high accuracy in recognizing static and
sequential gestures. Future systems aim to include lip and facial features for
enhanced recognition.
3. Machine Learning Methods for Sign Language Recognition
A review of vision-based SLR systems highlights the growing use of CNNs
and wearable devices. Challenges include complex backgrounds and low-
resolution images.
 5. Methodology
5.1 Dataset
• ASL Alphabet Dataset: Contains 520 images of hand gestures representing 26
English alphabets (A-Z).
• Custom Dataset: Captured using OpenCV, comprising 30 images per gesture
for 26 alphabets and 10 digits.
5.2 Data Preprocessing
• Convert images from BGR to RGB format.

• Resize images to a uniform dimension of 300x360 pixels.

• Noise removal and normalization.

5.3 Hand Landmark Detection

Utilized MediaPipe to extract 21 3D hand landmarks for each frame. The
BlazePalm detector isolates the palm, reducing computational overhead.
5.4 Gesture Recognition
• Keypoints from hand landmarks are fed into deep learning models:

o CNN for feature extraction.

o LSTM for temporal sequence analysis.

• The model is trained using labeled datasets and tested in real-time.

6. Results
The proposed system achieved the following results:
Metric value
Accuracy 95.5%
precision 96.8%
Recall 97.2%
F1-Score 98.0%

The model successfully translated static gestures into text in real-time,

demonstrating robustness against variations in lighting and background.
 7. Conclusion
This project demonstrates the potential of machine learning in enhancing
communication for individuals with hearing and speech impairments. The system
effectively recognizes and translates static gestures, providing a cost-effective and
accessible solution.
Future work includes:
• Expanding the system to dynamic gestures.

• Supporting multiple sign languages.

• Integrating facial expression recognition for enhanced accuracy.

• References
1. (ICITIIT), 2021, pp. 1-6, doi:10.1109/ICITIIT51526.2021.9399594.
Available: ML Based Sign Language Recognition System | IEEE Conference
Publication
2. M. WurangianMittal, A., et al. "A Modified LSTM Model for Continuous Sign
Language Recognition." IEEE Sensors Journal, 2019.
3. Sharma, V., et al. "Dynamic Two-Hand Gesture Recognition using CNN-
LSTM." IEEE iSES, 2021.
4. OpenCV Documentation, https://opencv.org.
5. MediaPipe by Google AI, https://mediapipe.dev.
6. Graves, A., et al. "Speech Recognition with Deep Recurrent Neural Networks."
CoRR, 2013.
7. K. Cheng, “Top 10 & 25 American sign language signs for beginners – the most
know top 10 & 25 ASL signs to learn first: Start ASL,” Start ASL Learn
American Sign Language with our Complete 3-Level Course!, 29-Sep-2021.
[Online]. Available:
8. Top 10 & 25 American Sign Language Signs for Beginners – The Most Know
Top 10 & 25 ASL Signs to Learn First.
A. Mittal, P. Kumar, P. P. Roy, R. Balasubramanian, and B.B. Chaudhuri,
"A Modified LSTM Model for Continuous Sign Language Recognition
Using Leap Motion," in IEEE Sensors Journal, vol. 19, no. 16, pp. 7056-
7063, 15 Aug.15, 2019.
9. “Real time sign language detection with tensorflow object detection and Python
| Deep Learning SSD,” YouTube, 05-Nov-2020. [Online]. Available:
https://www.youtube.com/watch?v=pDXdlXlaCco&t=1035s.
10. V. Sharma, M. Jaiswal, A. Sharma, S. Saini and R. Tomar, "Dynamic
Two Hand Gesture Recognition using CNN-LSTM based networks," 2021
.