How does supervised learning work in sign language?

Introduction

The algorithm learns to map input features to output labels by searching for patterns in the data. The model is trained on a dataset of videos or images of people signing, along with corresponding text or speech transcripts. Once the model is delivered, it can be used to recognize and translate sign language in real time.

What is sign language?

Sign language is a complete and complex language, with its own grammar, vocabulary and structure. Signs are formed using a combination of different elements, including hand shape, hand position, hand movement and facial expression.

Sign language is an important form of communication for deaf and hard of hearing people. It allows them to communicate effectively with others and participate fully in society.

How could it be implemented?

Sign Language Translation Apps

They use machine learning to translate sign language to text or voice in real time. This can help people who are deaf or hard of hearing communicate with people who hear.

Method:

import cv2
import mediapipe as mp

mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils

def translate_lenguage_senas(modelo):
cap = cv2.VideoCapture(0)
with mp_hands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5) as hands:
while cap.isOpened():
ret, frame = cap.read()
if not ret:
continue

# convertion
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Process the image and draw hand marks
results = hands.process(image)
if results.multi_hand_landmarks is not None:
for hand_landmarks in results.multi_hand_landmarks:
mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)

cv2.imshow(‘Sign Language Translator’, frame)
# print(translate)

if cv2.waitKey(5) & 0xFF == 27:
break

cap.release()
cv2.destroyAllWindows()

Sign Language Learning Apps

They use machine learning to teach people how to sign. They can provide information about the shapes and movements of the user’s hands, and help them learn new signs.

Method:

import tkinter as tk
from tkinter import PhotoImage
import pygame

sign_language_alphabet = {
‘A’: ‘a.wav’,
‘B’: ‘b.wav’,
‘C’: ‘c.wav’,
‘D’: ‘d.wav’,
‘E’: ‘e.wav’,

}

def play_sound(letter):
sound_file = sign_language_alphabet.get(letter)
if sound_file:
pygame.mixer.music.load(sound_file)
pygame.mixer.music.play()

def show_letter(letter):
label.config(image=letter_images[letter])
label.image = letter_images[letter]

app = tk.Tk()
app.title(“”)

letter_images = {}
for letter in sign_language_alphabet:
image = PhotoImage(file=f”images/{letter}.gif”)
letter_images[letter] = image

label = tk.Label(app)
label.pack()

for letter in sign_language_alphabet:
letter_button = tk.Button(app, text=letter, command=lambda l=letter: [show_letter(l), play_sound(l)])
letter_button.pack(side=tk.LEFT)

app.mainloop()

Sign language interpretation services:

provide real-time sign language interpretation during events and meetings. This can help make these events and meetings more accessible to people who are deaf or hard of hearing.

Method:

import socket
import threading

users = {}

def handle_client(client_socket):

username = client_socket.recv(1024).decode()


users[username] = client_socket

while True:

message = client_socket.recv(1024).decode()

if message.lower() == “quit”:
del users[username]
client_socket.close()
break


for user, socket in users.items():
if user != username:
socket.send(f”{username}: {message}”.encode())

host = “0.0.0.0”
port = 5555

server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.bind((host, port))
server.listen(5)

print(“[*] Interpretation Server running “)

while True:
client, addr = server.accept()
print(f”[*] Connection accepted from {addr[0]}:{addr[1]}”)
client_handler = threading.Thread(target=handle_client, args=(client,))
client_handler.start()

Conclusions:

Improves accessibility: Sign recognition models can help deaf and hard of hearing people communicate with hearing people. Sign translation models can help hearing people understand sign language.

Reduces costs: Sign recognition and sign translation models can reduce the need for sign language interpreters.

Improves quality: Sign recognition and sign translation models can improve the accuracy and reliability of communication.

References:

Kaggle: Your Machine Learning and Data Science Community

scikit-learn: machine learning in Python — scikit-learn 1.3.2 documentation