Update README.md

Author: CodingSamrat

README.md

@@ -1,5 +1,5 @@
 ![MasterHead](docs/SLR.png) 
-# Sign language Recognition <hr>
+# Sign language Recognition 
 
 This repository contains the source code and resources for a Sign Language Recognition System. The goal of this project is to develop a computer vision system that can recognize and interpret sign language gestures in real-time.
 
@@ -20,7 +20,7 @@ Though the project name is `Sign Language Recognition`, it can be used for any h
 
 <br><br>
 
-## Introduction <hr>
+## Introduction 
 Sign language is a visual means of communication used by individuals with hearing impairments. This project aims to bridge the communication gap by developing an automated system that can understand and interpret sign language gestures. The system utilizes computer vision techniques and machine learning algorithms to recognize and translate these gestures into text or speech.
 
 The Sign Language Recognition System consists of several components:
@@ -36,7 +36,7 @@ The Sign Language Recognition System consists of several components:
 
 <br><br>
 
-## Installetion <hr>
+## Installetion 
 To set up the Sign Language Recognition System on your local machine, follow these steps:
 
 1. Clone the repository to your local machine.
@@ -65,7 +65,7 @@ You are now ready to use the Sign Language Recognition System on your local mach
 
 <br><br>
 
-## Usage <hr>
+## Usage 
 To use the Sign Language Recognition System, follow these steps:
 
 1. Ensure that the required dependencies and resources are properly installed and set up.
@@ -81,13 +81,13 @@ To use the Sign Language Recognition System, follow these steps:
 
 Here is a demo of the Sign Language Recognition System in action:
 
-<img  alt="Coding" width="48%" src="docs/demo-ss-a.png">
-<img  alt="Coding" width="48%" src="docs/demo-ss-s.png">
+<img  alt="Coding" width="45%" src="docs/demo-ss-a.png">
+<img  alt="Coding" width="45%" src="docs/demo-ss-s.png">
 
 
 <br><br>
 
-## Customization <hr>
+## Customization 
 The Sign Language Recognition System is fully customizable and can be trained to recognize any hand gesture. To customize the system, follow these steps:
 
 1. Run the `app.py` file to open the application.
@@ -101,15 +101,15 @@ Enjoy your customized Sign Language Recognition System!
 
 <br><br>
 
-## System Overview <hr>
+## System Overview 
 In order to build such systems, data (keypoints of hands) must be obtained, then features involved in sign making must be extracted and finally combination of features must be analysed to describe the performed sign.
 
 ![System Overview](docs/flow-chart.png)
 
 
 <br><br>
 
-## Data Collection <hr>
+## Data Collection 
 The first step in building a sign language recognition system is to collect a dataset of sign language gestures. The dataset is used to train the machine learning model to recognize and interpret these gestures.
 
 For this project we use our hand sign data. And the data is collected using the [MediaPipe](https://google.github.io/mediapipe/) library. The library provides a hand tracking solution that can detect and track 21 hand landmarks in real-time. The hand landmarks are the key points or 2D coordinates that can be used to determine the pose of the hand.
@@ -121,16 +121,16 @@ The hand landmarks are used to extract features from the hand gestures. The feat
 
 <br><br>
 
-## Preprocessing <hr>
+## Preprocessing 
 The collected data is preprocessed to enhance the quality, remove noise, and extract relevant features. The preprocessing steps include:
 
 1. Getting the hand landmarks from the video stream.
 
 2. Converting the hand landmarks relative to the `wrist` landmark's coordinate `(0, 0)`. This is done by subtracting the `wrist` landmark's coordinate from all the other landmarks.
  
 <d>
-    <img  alt="Coding" width="48%" src="docs/hand_landmarks_o.jpg">
-    <img  alt="Coding" width="48%" src="docs/hand_landmarks_r.jpg">
+    <img  alt="Coding" width="45%" src="docs/hand_landmarks_o.jpg">
+    <img  alt="Coding" width="45%" src="docs/hand_landmarks_r.jpg">
 </d>
 
 3. Flatten the normalized hand landmarks to a 1 Dimensional list.
@@ -167,7 +167,7 @@ The collected data is preprocessed to enhance the quality, remove noise, and ext
 
 <br><br>
 
-## Model Training <hr>
+## Model Training 
 The preprocessed data is used to train a machine learning model to recognize and interpret sign language gestures. The model is trained using a convolutional neural network (CNN) architecture. The CNN is trained on the preprocessed data to learn the mapping between input gestures and their corresponding meanings.
 
 1. Obtain or create a sign language dataset.
@@ -185,15 +185,15 @@ The preprocessed data is used to train a machine learning model to recognize and
 
 <br><br>
 
-## Results <hr>
+## Results 
 The model was trained on a dataset of 24,000 hand gestures. The dataset was split into training and validation sets with a ratio of 80:20. The model was trained for 100 epochs with a batch size of 180. The training and validation accuracy and loss were recorded for each epoch.
 
 Our Proposed Model achieved an accuracy of `71.12%` on the validation set and `90.60%` on the testing set. The model was able to recognize and interpret sign language gestures in real-time with an accuracy of `71.12%`.
 
 
 <br><br>
 
-## Contributing <hr>
+## Contributing
 Contributions to this project are welcome. If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request. Let's work together to make the Sign Language Recognition System even better!
 
 We appreciate your contributions, whether big or small, and we look forward to working together to enhance the Sign Language Recognition System. Let's make a positive impact on the lives of individuals with hearing impairments and promote inclusivity in communication.