Sign Language Interpreter using Deep Learning
A sign language interpreter using live video feed from the camera. The project was completed in 24 hours as part of HackUNT-19, the University of North Texas's annual Hackathon. You can view the project demo on YouTube.
Table of contents
- General info
- Technologies and Tools
- Code Examples
The theme at HACK UNT 19 was to use technology to improve accessibility by finding a creative solution to benefit the lives of those with a disability. We wanted to make it easy for 70 million deaf people across the world to be independent of translators for there daily communication needs, so we designed the app to work as a personal translator 24*7 for the deaf people.
The entire demo of the project can be found on YouTube.
Technologies and Tools
- Use comand promt to setup environment by using install_packages.txt and install_packages_gpu.txt files.
pyton -m pip r install_packages.txt
This will help you in installing all the libraries required for the project.
set_hand_histogram.pyto set the hand histogram for creating gestures.
- Once you get a good histogram, save it in the code folder, or you can use the histogram created by us that can be found here.
- Added gestures and label them using OpenCV which uses webcam feed. by running
create_gestures.pyand stores them in a database. Alternately, you can use the gestures created by us here.
- Add different variations to the captured gestures by flipping all the images by using
load_images.pyto split all the captured gestures into training, validation and test set.
- To view all the gestures, run
- Train the model using Keras by running
final.py. This will open up the gesture recognition window which will use your webcam to interpret the trained American Sign Language gestures.
# Model Traiining using CNN import numpy as np import pickle import cv2, os from glob import glob from keras import optimizers from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers import Flatten from keras.layers.convolutional import Conv2D from keras.layers.convolutional import MaxPooling2D from keras.utils import np_utils from keras.callbacks import ModelCheckpoint from keras import backend as K K.set_image_dim_ordering('tf') os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' def get_image_size(): img = cv2.imread('gestures/1/100.jpg', 0) return img.shape def get_num_of_classes(): return len(glob('gestures/*')) image_x, image_y = get_image_size() def cnn_model(): num_of_classes = get_num_of_classes() model = Sequential() model.add(Conv2D(16, (2,2), input_shape=(image_x, image_y, 1), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same')) model.add(Conv2D(32, (3,3), activation='relu')) model.add(MaxPooling2D(pool_size=(3, 3), strides=(3, 3), padding='same')) model.add(Conv2D(64, (5,5), activation='relu')) model.add(MaxPooling2D(pool_size=(5, 5), strides=(5, 5), padding='same')) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(num_of_classes, activation='softmax')) sgd = optimizers.SGD(lr=1e-2) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) filepath="cnn_model_keras2.h5" checkpoint1 = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') callbacks_list = [checkpoint1] #from keras.utils import plot_model #plot_model(model, to_file='model.png', show_shapes=True) return model, callbacks_list def train(): with open("train_images", "rb") as f: train_images = np.array(pickle.load(f)) with open("train_labels", "rb") as f: train_labels = np.array(pickle.load(f), dtype=np.int32) with open("val_images", "rb") as f: val_images = np.array(pickle.load(f)) with open("val_labels", "rb") as f: val_labels = np.array(pickle.load(f), dtype=np.int32) train_images = np.reshape(train_images, (train_images.shape, image_x, image_y, 1)) val_images = np.reshape(val_images, (val_images.shape, image_x, image_y, 1)) train_labels = np_utils.to_categorical(train_labels) val_labels = np_utils.to_categorical(val_labels) print(val_labels.shape) model, callbacks_list = cnn_model() model.summary() model.fit(train_images, train_labels, validation_data=(val_images, val_labels), epochs=15, batch_size=500, callbacks=callbacks_list) scores = model.evaluate(val_images, val_labels, verbose=0) print("CNN Error: %.2f%%" % (100-scores*100)) #model.save('cnn_model_keras2.h5') train() K.clear_session();
Our model was able to predict the 44 characters in the ASL with a prediction accuracy >95%.
Features that can be added:
- Deploy the project on cloud and create an API for using it.
- Increase the vocabulary of our model
- Incorporate feedback mechanism to make the model more robust
- Add more sign languages
Project is: finished. Our team was the winner of the UNT Hackaton 2019. You can find the our final submission post on devpost. If you would like us to implement the project end-to-end for you please book a session.
If you loved what you read here and feel like we can collaborate to produce some exciting stuff, or if you just want to shoot a question, please feel free to connect with me on email, LinkedIn, or Twitter. My other projects can be found here.