How to Build a Real-Time Sign Language Interpreter Using MATLAB and Transfer Learning
However, many people who interact with them—whether in schools, homes, or public spaces—often do not understand it. American Sign Language (ASL), one of the most widely used sign languages in the world, still faces accessibility challenges due to the lack of available interpreters. To address this gap, MATLAB can be used to develop a real-time sign language interpreter that recognizes ASL alphabets using webcam input and deep learning techniques. This blog explores how students and beginners can use MATLAB, combined with Transfer Learning and the AlexNet model, to build a complete solution from scratch. The project includes steps for creating a custom dataset, training a neural network, and implementing real-time testing—all within the MATLAB environment. If you're working on a similar project or need help to solve your MATLAB assignment, this guide will serve as a practical reference. With a strong focus on accessibility, the solution also opens the door to impactful innovations that can improve communication for millions. This hands-on project is ideal for those looking to learn, build, and contribute to socially meaningful technology using MATLAB.
How the Idea Originated
The core motivation behind this project was the lack of easy-to-access tools for understanding ASL in real time. Sign language is rich, visual, and expressive, but the language barrier often isolates deaf individuals in public and even within their own families. By creating a deep learning model capable of recognizing static alphabet signs through a webcam, the project aims to support interactive learning, especially for children. It brings both educational and accessibility value to classrooms, homes, and even public spaces.
How the Problem Was Broken Down
The project was structured in three essential phases. First, the dataset needed to be created manually, as existing datasets didn’t match the image size and input format required by AlexNet. Second, a neural network would be trained using Transfer Learning, allowing a pre-trained model to adapt to a new task with limited data and time. Third, the model would be tested in real time, using a webcam to classify hand gestures and output recognized characters. Each step played a crucial role in creating a seamless and responsive system.
How the Dataset Was Created
Since most public datasets were either too complex or didn’t fit the 227x227 pixel input format required by AlexNet, a custom dataset was built from scratch. Around 300 images were captured per alphabet letter using MATLAB’s USB webcam support package. A designated processing area on the screen helped keep hand gestures in a consistent location, and efforts were made to control lighting and background for better model performance. The image collection process used a simple while loop to automate the capture and storage of 300 images per character. Here’s the code that was used for dataset creation:
c = webcam; % Create the Camera Objectx = 0; y = 0; height = 300; width = 300;bboxes = [x y height width];temp = 0;while temp <= 300 e = c.snapshot; IFaces = insertObjectAnnotation(e, 'rectangle', bboxes, 'Processing Area'); imshow(IFaces); filename = strcat(num2str(temp), '.bmp'); es = imcrop(e, bboxes); es = imresize(es, [227 227]); imwrite(es, filename); temp = temp + 1; drawnow;endclear c;
This script automates the process of capturing and resizing images to prepare them for training while keeping the webcam connection live until the desired number of samples is collected.
How the Model Was Trained Using Transfer Learning
Instead of building a neural network from scratch — which would require thousands of images and a longer training cycle — the model was trained using Transfer Learning. AlexNet, a powerful convolutional neural network, was used as the base model. Originally trained on over a million images from the ImageNet database, AlexNet serves as a feature extractor in this project. Only the final layers of the network were modified to suit the ASL classification task, replacing them with new layers compatible with the number of classes being trained. The training process involved creating an imageDatastore, setting the training options, and using the trainNetwork function to fine-tune the model on the custom dataset. The following code was used for training:
g = alexnet;layers = g.Layers;% Modify the networklayers(23) = fullyConnectedLayer(10); % Assuming 10 classes for simplicitylayers(25) = classificationLayer;% Load imagesallImages = imageDatastore('testing', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');% Set training optionsopts = trainingOptions('sgdm', ... 'InitialLearnRate', 0.001, ... 'MaxEpochs', 20, ... 'MiniBatchSize', 64);% Train the networkmyNet1 = trainNetwork(allImages, layers, opts);% Save the trained modelsave myNet1;
The training was completed within minutes due to the advantage of reusing AlexNet’s pretrained weights. This enabled the model to achieve good performance with a relatively small dataset.
How Real-Time Testing Was Implemented
Once training was complete, the next step was to test the system in real-time using a webcam. The setup involves reconnecting to the webcam, defining the same image-cropping area, resizing the frame to 227x227, and then feeding the image into the trained model for classification. This loop continuously displays the recognized label on screen and updates it as new gestures appear in the camera frame. The implementation of the real-time prediction system is shown in the code below:
load myNet1; % Load the trained modelc = webcam;x = 0; y = 0; height = 300; width = 300;bboxes = [x y height width];while true e = c.snapshot; IFaces = insertObjectAnnotation(e, 'rectangle', bboxes, 'Processing Area'); es = imcrop(e, bboxes); es = imresize(es, [227 227]); label = classify(myNet1, es); imshow(IFaces); title(char(label)); drawnow;end
This code creates a continuous loop that displays the camera feed with a rectangular box indicating the processing area and the predicted letter appearing in real-time. The fluid response and ease of implementation made it a powerful proof of concept for recognizing ASL alphabets.
How Effective Was the Model
The model demonstrated excellent recognition capability for most alphabet gestures when tested under consistent lighting and a clean background. The accuracy of predictions was greatly improved by ensuring uniform conditions during both training and testing phases. For example, the letter "A" could be correctly classified almost every time it was shown. This proved that even a weekend-built prototype could yield highly usable results, especially for educational and assistive technology use cases. The success highlighted how careful dataset preparation and the right model architecture can deliver reliable results even with modest computing resources.
Why MATLAB Was Chosen
MATLAB was the natural choice for this project due to its simplicity, visual tools, and robust deep learning support. Tasks like image acquisition, annotation, and visualization are far more straightforward in MATLAB than in many other programming environments. The Deep Learning Toolbox makes importing and modifying pretrained networks easy, while functions like imshow, insertObjectAnnotation, and classify simplify both debugging and deployment. Additionally, MATLAB’s integration with webcams, support for image processing, and GPU acceleration significantly reduce development time for real-time applications. All these features combined made MATLAB an ideal platform for building and testing this sign language recognition system.
How Hackathons Encourage Learning
This project began as an idea for a university hackathon. Hackathons provide an exciting opportunity to explore new technologies and apply knowledge in a high-energy, collaborative setting. In a short span, participants can build something meaningful, meet people with similar interests, and learn through hands-on experimentation. This particular project was conceptualized, developed, and demonstrated over a weekend. The rapid pace not only enhanced technical learning but also provided practical experience in problem-solving, teamwork, and presentation.
How You Can Extend This Project
While this version of the interpreter handles only static ASL alphabet signs, the project can be significantly expanded. One way to extend the model is by including dynamic gestures, which require capturing motion over time and using recurrent networks like LSTM. Another idea is to upgrade from AlexNet to deeper networks like ResNet or MobileNet for improved accuracy. The trained model can also be converted into portable applications using MATLAB Coder or deployed to embedded devices and smartphones. Additionally, support for other sign languages like British or Indian Sign Language could make the project more inclusive. Features like text-to-speech or full sentence interpretation could also transform this prototype into a full-scale communication tool.
How This Helps in Real-Life Applications
The real-world applications of this system are numerous and impactful. In educational environments, it can be used as an interactive learning tool for both hearing and deaf students. At public service centers, such tools could help bridge communication gaps between staff and deaf visitors. In healthcare, doctors could use similar applications to understand basic needs of patients who use sign language. Additionally, families with deaf members could use this system at home to ease communication and encourage language learning for children. The simplicity and cost-effectiveness of the solution also mean that it can be replicated and distributed widely with minimal technical barriers.
Final Thoughts
Building a sign language interpreter using MATLAB and Transfer Learning is a rewarding experience that combines technical skill with social impact. The ability to recognize hand gestures in real-time opens doors to a variety of educational, assistive, and interactive applications. Through this project, it becomes clear that MATLAB is not just for engineers or researchers—it’s a powerful tool for students, innovators, and anyone looking to make technology more accessible. Whether you're working on a course assignment or planning your next hackathon project, this is a great example of how you can turn code into something meaningful that makes a difference.