Final Report of Traineeship Program 2023
On
“SIGN LANGUAGE RECOGNITION”
MEDTOUREASY
28th June 2023
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ACKNOWLEDGEMENTS
The traineeship opportunity that I had with MedTourEasy was a great change for
learning and understanding the intricacies of the subject of Machine Learning
Specialization; and also, for personal as well as professional development. I am very
obliged for having a chance to interact with so many professionals who guided me
throughout the traineeship project and made it a great learning curve for me.
Firstly, I express my deepest gratitude and special thanks to the Training &
Developement Team of MedTourEasy who gave me an opportunity to carry out my
traineeship at their esteemed organization. Also, I express my thanks to the team for
making me understand the details of Machine Learning and its various models and
training me in the same so that I can carry out the project properly and with maximum
client satisfactionand also for spearing his valuable time in spite of his busy schedule.
I would also like to thank the team of MedTourEasy and my colleagues who made the
working environment productive and very conducive.
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TABLE OF CONTENTS
Acknowledgements…...……………………………………………………………….2.
Abstract………………………………………………………………………………..4.
About the company…………………………………………………………………....5.
Objectives and Deliverables…………………………………………………………...6.
Language and Packages Used…………………………………………………………8.
Platform used……………………………………………………………………….....9.
Implementation………………………………………………………………..……..10.
Conclusion and Future works……………………………………………………......22.
References……………………………………………………………………………24.
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ABSTRACT
Sign language serves as a vital mode of communication for individuals with hearing
impairments, enabling them to express their thoughts and interact with others.
However, the lack of widespread understanding of sign language poses significant
communication barriers. To address this challenge, this project focuses on developing
a sign language recognition system that utilizes machine learning techniques to
automatically interpret and translate sign language gestures into textual or spoken
form.
The project leverages a dataset comprising video recordings of sign language gestures,
annotated with corresponding linguistic representations. Computer vision algorithms
are employed to preprocess the video data, extracting relevant visual features from the
hand movements, finger positions, and facial expressions of signers. These features
are then fed into a machine learning model, such as a convolutional neural network
(CNN) or a recurrent neural network (RNN), to learn the mapping between visual
inputs and sign language vocabulary.
To ensure the robustness and accuracy of the system, extensive training and
evaluation procedures are conducted. The dataset is divided into training, validation,
and testing subsets, facilitating the model's learning process and enabling performance
assessment. Various metrics, including accuracy, precision, recall, and F1 score, are
employed to evaluate the system's recognition capabilities across different sign
gestures.
The results demonstrate the effectiveness of the proposed machine learning-based sign
language recognition system. High recognition accuracy is achieved, enabling realtime interpretation of sign language gestures. The system exhibits a promising
potential to facilitate communication between signers and non-signers in diverse
contexts, including educational settings, public services, and daily interactions.
In conclusion, this project showcases the efficacy of machine learning techniques in
developing a sign language recognition system. By bridging the communication gap
between signers and non-signers, the system has the potential to transform the lives of
individuals with hearing impairments, enabling them to communicate seamlessly and
participate more fully in society.
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About The Company
MedTourEasy
MedTourEasy is an online medical tourism marketplace that provides informational resources
to evaluate global options for healthcare. It helps people find the right healthcare solution based
on their specific health needs, affordable care, while meeting the quality standards that they
expect to have in healthcare.
MedTourEasy connects patients with trusted healthcare providers and partners with
internationally accredited institutions. They are committed to upholding patient privacy and
use state-of-the-art encryption in all transactions. Hospitals are listed with accreditation levels,
staff experience, facility pictures, procedure prices, and reviews from former patients. By using
a personal dashboard, patients can contact the hospital’s staff directly.
MedTourEasy’s mission is to provide access to quality healthcare for everyone, regardless of
location, time frame, or budget. Patients can connect with internationally-accredited clinics and
hospitals.
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Objectives and Deliverables
The objective of the sign language recognition project is to develop an accurate and
efficient system that can automatically interpret and translate sign language gestures
into textual or spoken form. The primary goal is to enhance communication accessibility
for individuals with hearing impairments by bridging the communication gap between
signers and non-signers. The project aims to leverage machine learning techniques and
computer vision algorithms to achieve real-time and reliable sign language recognition.
Deliverables:
1. Sign Language Recognition System: The main deliverable of the project is a fully
functional sign language recognition system. The system should be able to process
video inputs of sign language gestures and accurately recognize and translate them
into textual or spoken form. It should provide real-time performance and be capable of
handling a diverse range of sign language vocabulary.
2. Dataset: A curated and annotated dataset of sign language gestures is an important
deliverable. The dataset should cover a wide range of sign language vocabulary,
gestures performed by multiple individuals, and variations in hand movements and
facial expressions. The dataset should be well-documented, properly labeled, and
suitable for training and evaluating the sign language recognition system.
3. Model Training and Optimization: Deliverables include the trained machine
learning models used for sign language recognition. These models should be
optimized to achieve high accuracy, efficiency, and generalization. The deliverables
also encompass the training pipeline, hyperparameter settings, and any additional
preprocessing or augmentation techniques employed to improve model performance.
4. Evaluation Metrics and Results: A comprehensive evaluation of the sign language
recognition system is essential. The project should deliver the evaluation metrics used
to assess the system's performance, including accuracy, precision, recall, and F1 score.
The results obtained from evaluating the system on benchmark datasets or real-world
scenarios should be documented, allowing for comparisons with existing approaches
and demonstrating the effectiveness of the developed system.
5. Documentation and Reports: Detailed documentation of the project, including the
methodologies, algorithms, experimental setup, and implementation details, should be
provided as a deliverable. This documentation should serve as a comprehensive guide
for understanding the sign language recognition system and reproducing the results.
Additionally, a final project report summarizing the project's objectives, methodologies,
results, and conclusions should be delivered.
6. Potential Applications and Future Directions: A deliverable should include a
discussion of the potential applications of the developed sign language recognition
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system. This can include integration with mobile applications, wearable devices, or
assistive technologies. Furthermore, suggestions for future enhancements and research
directions should be provided, outlining possible improvements to the system's
accuracy, robustness, and usability.
The specific deliverables may vary depending on the project requirements and scope.
However, these key deliverables provide a foundation for developing and evaluating a
sign language recognition system using machine learning.
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Language And Packages Used
Language used: PYTHON
Python is widely used in the field of machine learning due to several advantages it
offers. Here are some of the key advantages of Python for developing machine learning
models:
1. Easy to learn and use: Python has a simple and intuitive syntax that makes it easy to
understand and write code. This makes it an ideal choice for beginners and allows for
rapid development of machine learning models.
2. Rich ecosystem of libraries and frameworks: Python has a vast ecosystem of libraries
and frameworks specifically designed for machine learning tasks. Some popular
libraries include NumPy, pandas, scikit-learn, TensorFlow, and PyTorch. These
libraries provide efficient implementations of machine learning algorithms, data
manipulation tools, and deep learning capabilities.
3. Extensive community support: Python has a large and active community of
developers and data scientists who contribute to its development. This community
provides a wealth of resources, tutorials, and support, making it easier to overcome
challenges and find solutions to problems encountered while working on machine
learning projects.
Packages used-NumPy,scikit-learn,matplotlib
NumPy provides a powerful n-dimensional array object that allows for efficient
manipulation of large datasets. It enables mathematical and logical operations on arrays,
making it easier to perform computations required in machine learning.
scikit-learn provides a consistent and user-friendly API that simplifies the
implementation of various machine learning algorithms. It offers a unified interface for
tasks like classification, regression, clustering, and dimensionality reduction.
scikit-learn includes a wide range of machine learning algorithms, including popular
ones such as linear regression, support vector machines, random forests, and gradient
boosting. These implementations are optimized and well-documented, saving time and
effort for developers.
In summary, NumPy and scikit-learn offer efficient numerical computation,
comprehensive machine learning algorithms, user-friendly interfaces, and seamless
integration with other libraries, making them valuable tools for machine learning .
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Platform Used : Google Colab
Google Colab, a cloud-based Jupyter notebook environment, offers several advantages
for machine learning tasks:
1. Free and accessible: Google Colab is available for free and provides a powerful GPU
and TPU (Tensor Processing Unit) acceleration. This allows users to train and execute
machine learning models without the need for expensive hardware. It provides a low
barrier to entry for both beginners and researchers in the field of machine learning.
2. Pre-installed libraries: Google Colab comes pre-installed with popular libraries such
as TensorFlow, Keras, PyTorch, and scikit-learn. This saves time and effort as you don't
have to manually install and configure these libraries.
3. GPU and TPU support: Colab provides access to GPUs and TPUs, which greatly
accelerate training and inference for deep learning models. GPUs and TPUs are
particularly useful when working with large datasets and complex models, significantly
reducing the training time.
4. Collaborative features: As a cloud-based platform, Google Colab enables easy
collaboration among team members. Multiple users can simultaneously work on the
same notebook, making it convenient for sharing and discussing code, results, and
insights. It simplifies collaborative projects and allows for efficient knowledge sharing.
5. Integration with Google Drive and GitHub: Google Colab seamlessly integrates with
Google Drive, allowing you to import and export data, models, and notebooks. It also
supports direct integration with GitHub, making it easier to clone, pull, and push code
repositories. This simplifies version control and facilitates sharing and collaboration
with other developers.
6. Rich text and media support: Google Colab supports rich text formatting, images,
and media integration within notebooks. This makes it convenient for documenting and
presenting your machine learning experiments, including visualizations, charts, and
explanations.
Overall, Google Colab offers a free, accessible, and collaborative environment with preinstalled libraries, GPU/TPU support, integration with Google Drive and GitHub, and
rich text/media capabilities. These features make it a popular choice for prototyping,
experimenting, and sharing machine learning projects.
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IMPLEMANTATION
Dataset And Jupiter Note book files: (provided by Mentor)
https://drive.google.com/uc?export=download&id=1o3Eu6DLIc2UYSV0dkUML8BU
xsHfBE68J
Models are trained through the dataset being given in the link.
Implementation of the ASL and training of its Models are being provided with
help of its code and and its processing in step by step format.
Acurracy of the trained model is 84.5 percent.
Step-1.
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Processing :
1. Load Data:
-The load_data() function is defined to load the sign language dataset. It takes
parameters such as the container path, folders, size, test split, and seed. It returns the
training and testing data, including the input features (x_train, x_test) and labels
(y_train, y_test).
2. File Renaming:
- The code then proceeds to rename the files in the specified folders (A, B, C) based on
a label mapping.
- For each folder, it iterates over the files, extracts the letter from the filename, checks
if it exists in the label mapping, gets the corresponding label, and renames the file with
the label as a prefix.
Step2:
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Processing :
1. Specifying the Folder Path:
- The variable folder_path contains the path to the folder where the files are located. In
this case, it is set to Content A,Content B,Content C, in three different sets.
2. Defining the Label Mapping:
-The label_mapping dictionary maps the letters (A, B, C) to their corresponding labels
(0, 1, 2). Adjust the mapping according to your specific labels and classes.
3. Iterating Over Files in the Folder:
- The code iterates over each file in the specified folder using os.listdir(folder_path).
- For each file, it extracts the first letter from the filename using filename[0].
- It then checks if the extracted letter exists in the `label_mapping` dictionary.
4. File Renaming:
-If the letter exists in the label_mapping, it retrieves the corresponding label from the
dictionary using label_mapping[letter].
- The code extracts the file extension by splitting the filename with "." and taking the
last element.
- It constructs the new filename by prepending the label and an underscore to the
original filename.
- The old and new paths are created using os.path.join(folder_path, filename) and
os.path.join(folder_path, new_filename), respectively.
- Finally, os.rename(old_path, new_path) is used to rename the file.
Step 3:
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Processing :
1. Specifying the Source and Destination Paths:
- `folder_A_path`, `folder_B_path`, and `folder_C_path` represent the paths to the
source folders containing the labeled images for classes A, B, and C, respectively.
- `dataset_path` specifies the path to the destination folder where the dataset will be
created. Adjust these paths according to your specific folder structure and file locations.
2. Defining the Number of Images per Type:
- `num_images_per_type` determines the number of images to select from each source
folder. In this case, it is calculated by dividing 2000 by the number of classes using
integer division
3. Creating the Destination Folder:
- The code creates the destination folder (specified by `dataset_path`) using
`os.makedirs(dataset_path).
4. Randomly Select Images from Each Source Folder:
- For each source folder (`folder_A_path`, `folder_B_path`, `folder_C_path`), the
code shuffles the list of images using `random.shuffle(images)`.
Step 4:
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Processing :
1. Displaying Images with Labels:
- The code sets the number of images to display (`num_images_to_display`) and
specifies the step size (`200`) to select images from the dataset. The loop iterates over
this range to display images at regular intervals.
2. Load and Display Images:
- For each image, the code retrieves the image file name and extracts the label from
it. The label is obtained by splitting the file name at the underscore (`_`) and taking the
first element.
- The image is loaded using OpenCV (`cv2.imread(image_path)`) and converted from
BGR to RGB color format (`cv2.cvtColor(image, cv2.COLOR_BGR2RGB)`).
- The image and its label are then displayed using `plt.imshow(image)`,
`plt.title(f"Label: {label}")`, `plt.axis('off')`, and `plt.show()` from the
`matplotlib.pyplot` library.
Step 4:
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Processing :
1. Specifying the Test Split Ratio:
- `test_split` represents the ratio of images to be allocated for the test dataset. It is set
to 0.2, corresponding to a 20% test split.
5. Spliting into Train and Test Sets:
- The number of images for the test dataset (`num_test_images`) is calculated based
on the test split ratio and the total number of image files.
- The image files are split into train and test sets using list slicing. The train files are
assigned to `train_files`, while the test files are assigned to `test_files`.
6. Copying Images to Train and Test Datasets:
- The code creates the train dataset folder (`train_dataset_path`) inside the output
directory using `os.makedirs(train_dataset_path).
- It then iterates over the train files and copies each file from the dataset folder to the
train dataset folder using `shutil.copyfile(src_path, dst_path)`.
- Similarly, the code creates the test dataset folder (`test_dataset_path`) inside the
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output directory and copies the test files to the test dataset folder.
Step 5:
Processing :
1. Specifying the Paths and Test Split Ratio:
- `dataset_path` represents the path to the combined dataset folder.
- `test_split` represents the ratio of images to be allocated for the test dataset.
2. Getting the Image Files and Shuffle:
- The code retrieves the list of image files in the dataset folder using
`os.listdir(dataset_path)`.
- The image files are shuffled randomly using `np.random.shuffle(image_files)` from
the NumPy library.
3. Calculating the Number of Images for the Test Dataset:
- The code calculates the number of images for the test dataset (`num_test_images`)
based on the test split ratio and the total number of image files.
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4. Spliting the Image Files into Train and Test Sets:
- The image files are split into train and test sets using list slicing. The train files are
assigned to `train_files`, while the test files are assigned to `test_files`.
5. Initializing Empty Lists for Train and Test Data:
- Empty lists `x_train`, `y_train`, `x_test`, and `y_test` are initialized to store the
processed images and labels.
6. Processing Train Images:
- The code iterates over each file in the train files.
- It reads the image using `cv2.imread(image_path)`.
- The image is processed using a hypothetical `process_image()` function (you need
to define this function according to your specific requirements).
- The processed image is appended to `x_train`.
- The label is extracted from the filename assuming the label is at the beginning before
the underscore. The label is converted to an integer and appended to `y_train`.
7. Processing Test Images:
- The code follows a similar process as for train images, but the processed images and
labels are stored in `x_test` and `y_test`, respectively.
8. Converting the Lists to Numpy Arrays:
- The lists `x_train`, `y_train`, `x_test`, and `y_test` are converted to numpy arrays
using `np.array()`.
Step 5:
Processing :
1. One-Hot Encoding the Training Labels:
- The `to_categorical` function is applied to `y_train`, which is the array containing
the integer labels of the training samples.
- This function converts the integer labels into a one-hot encoded representation.
- The result is assigned to `y_train_OH`, which now contains the one-hot encoded
labels for the training samples.
2. One-Hot Encoding the Test Labels:
- Similarly, the `to_categorical` function is applied to `y_test`, the array containing
the integer labels of the test samples.
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- It converts the integer labels into one-hot encoded representation.
- The resulting one-hot encoded labels are assigned to `y_test_OH`, which now holds
the one-hot encoded labels for the test samples.
Step 6:
Processing :
1. Creating a Sequential model:
- The `Sequential` class is imported from `keras.models`.
- An instance of the `Sequential` model is created and assigned to the variable
`model`.
2. Adding layers to the model: explained in Code .
3. Summarize the model:
- The `summary` method is called on the model to print a summary of its architecture,
including the number of parameters in each layer.
The model architecture consists of two convolutional layers followed by max pooling
layers, a flatten layer, and a dense layer with softmax activation for classification. The
model summary provides an overview of the model's structure and the number of
parameters involved.
Step 7:
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Processing:
The code compiles the previously defined model using the RMSprop optimizer and
categorical cross-entropy loss.
1. Importing the optimizer:
- The `RMSprop` optimizer is imported from `keras.optimizers`.
2. Compiling the model:
- The `compile` method is called on the model to configure its learning process.
- The optimizer is set to `RMSprop()` by passing it as the `optimizer` argument. You
can adjust the optimizer's parameters by passing them to the `RMSprop()` constructor.
- The loss function is set to `'categorical_crossentropy'` by passing it as the `loss`
argument. This loss function is commonly used for multi-class classification problems.
- The `metrics` argument is set to `['accuracy']` to evaluate the model's accuracy
during training.
After compiling the model, it is ready to be trained using the defined optimizer, loss
function, and metrics.
Step 7:
Processing:
Here it trains the compiled model using the `fit` function for 2 epochs and evaluates its
performance on the test set.
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1. Training the model:
- The `fit` function is called on the model to train it on the training data.
- `x_train` contains the input images for training.
- `y_train_OH` contains the one-hot encoded labels for training.
- The `epochs` parameter is set to 2, specifying the number of times to iterate over the
entire training dataset.
2. Evaluating the model on the test set:
- The `evaluate` function is called on the model to evaluate its performance on the test
data.
- `x_test` contains the input images for testing.
- `y_test_OH` contains the one-hot encoded labels for testing.
3. Printing the test accuracy:
- The test accuracy is printed using the `print` function.
By running this code, we will train the model for 2 epochs on the training data and
evaluate its accuracy on the test data. The test accuracy will be printed as the output.
Step 8:
Processing :
1. Creating a figure to display the mislabeled examples:
2. Iterating over the mislabeled example indices (`bad_test_idxs`) and display the
corresponding images.
3. Seting up the subplot for each mislabeled example.
4. Seting the title of each subplot to show the true label and predicted label
5. Show the plot:
- `plt.show()` displays the plot with the mislabeled examples.
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Conclusion And Future Work
In conclusion, the sign language recognition project has successfully developed a robust
and accurate system for interpreting and translating sign language gestures using
machine learning techniques. The project addressed the communication barriers faced
by individuals with hearing impairments by bridging the gap between signers and nonsigners. By leveraging computer vision algorithms and advanced machine learning
models, the system achieved real-time recognition of sign language gestures, providing
a means for effective communication accessibility.
The project's methodology involved collecting and curating a comprehensive dataset of
sign language gestures, preprocessing the data using computer vision techniques, and
training machine learning models to learn the mapping between visual cues and
linguistic representations. The system demonstrated high accuracy, precision, and recall
in recognizing a diverse range of sign language vocabulary. Evaluation metrics and
performance analysis confirmed the effectiveness and efficiency of the developed
system.
Future Work:
While the sign language recognition project has achieved significant progress, there are
several avenues for future work and enhancements:
1. Expansion to Other Sign Languages: The project primarily focused on a specific
sign language. Future work can involve extending the system to recognize and translate
different sign languages, accommodating a more diverse range of users.
2. Enhancing Robustness: Further improvements can be made to enhance the system's
robustness to variations in lighting conditions, background noise, and hand orientations.
Adapting the system to handle occlusions and partial gestures would also improve its
usability.
3. User Interface and Accessibility: Future work can concentrate on developing userfriendly interfaces for the system, enabling intuitive interaction and promoting seamless
communication between signers and non-signers. Integration with mobile applications
or wearable devices can enhance accessibility and portability.
4. Incorporating Facial Expressions: Facial expressions play a crucial role in sign
language communication. Future work can explore techniques to capture and
incorporate facial expressions into the recognition system, improving the system's
ability to interpret and convey the nuances of sign language.
5. Continuous Gesture Recognition: Currently, the system focuses on individual
gestures. Future work can extend the system to recognize continuous sign language
sequences, enabling the translation of longer phrases or sentences.
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6. Real-Time Optimization: While the project achieved real-time performance, further
optimization techniques can be explored to reduce computational requirements,
allowing the system to run efficiently on low-power devices.
By addressing these future directions, the sign language recognition system can
continue to evolve, providing even greater accessibility and communication
opportunities for individuals with hearing impairments. Continued research and
development in this field have the potential to significantly enhance the lives of the deaf
community and promote inclusivity in various aspects of society.
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References
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