Update README.md

README.md

@@ -1 +1,57 @@
-# hand-gestures-recognition-and-segmentation-using-CNN-and-OpenCV
+# hand-gestures-recognition-and-segmentation-using-CNN-and-OpenCV
+
+This GitHub repository contains code for a Convolutional Neural Network (CNN) model to detect five different hand gestures from segmented hand photos. The model is trained on a dataset using Keras with TensorFlow backend and includes data preprocessing, model building, training, and evaluation. Additionally, there is a separate file, "Real_life_test," demonstrating real-time hand gesture recognition using OpenCV.
+
+## Project Structure
+
+The repository is organized as follows:
+
+1. **Data Preparation:**
+   - The dataset is stored in the "data" directory, with subdirectories for each class representing different hand gestures. You can download the dataset from [this Kaggle link](https://www.kaggle.com/datasets/sarjit07/hand-gesture-recog-dataset/data) and extract it into the "data" directory.
+
+2. **CNN Model:**
+   - The initial model architecture is defined using Keras Sequential API in the main file.
+   - The first attempt may show signs of overfitting, so a second model is created with dropout and regularization to address this issue.
+   - The third model utilizes a pre-trained ResNet50 model for feature extraction, followed by additional layers for classification.
+
+3. **Training and Evaluation:**
+   - The models are trained using the training set and evaluated on the validation and test sets.
+   - Training history, loss, and accuracy plots are visualized for each model.
+   - Confusion matrices and classification reports provide detailed performance metrics.
+
+4. **Real-time Hand Gesture Recognition:**
+   - The "OpenCV_test" file demonstrates how to use OpenCV for real-time hand gesture recognition.
+   - It captures video frames from the camera, preprocesses them, and utilizes the trained model to predict gestures.
+
+5. **Dataset Testing:**
+   - The "CNN02_Model_Test" file allows testing the trained model on any provided segmented hand photos.
+   - Provide the directory path containing the segmented hand photos, and the model will predict the gestures.
+
+## Instructions:
+
+1. **Dataset:**
+   - Download the dataset from [Kaggle](https://www.kaggle.com/datasets/sarjit07/hand-gesture-recog-dataset/data) and extract it into the "data" directory.
+   - I used all the training classes except the "blank" one
+
+2. **Training:**
+   - Run the "CNN_Train" file to train and evaluate the CNN models.
+   - Experiment with model architectures and hyperparameters to achieve optimal performance.
+
+3. **Real-time Testing:**
+   - Execute the "OpenCV_test" file to see real-time hand gesture recognition using OpenCV.
+
+4. **Model Testing on Segmented Photos:**
+   - Use the "Images_Test" file to test the trained model on segmented hand photos.
+   - Provide the directory path containing the photos, and the model will predict the gestures.
+
+5. **Model Saving:**
+   - The trained models are saved in the repository for later use.
+
+## Requirements:
+
+- Python 3.x
+- Libraries: TensorFlow, Keras, OpenCV, scikit-learn, matplotlib, seaborn
+
+Feel free to customize and extend the code according to your requirements. For any issues or suggestions, please create an issue in the repository.
+
+Happy coding! 🚀