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-# ASL Recognition App: Image Preprocessing and Feature Extraction
+# ASL Recognition App
 
-This document explains the image preprocessing and feature extraction techniques used in our ASL Recognition App.
+This application recognizes American Sign Language (ASL) hand signs using image processing and machine learning techniques.
 
-## Image Preprocessing
+## How it works
 
-1. **Image Loading**: We use OpenCV (cv2) to load and process images.
-2. **Color Conversion**: Images are converted from BGR to RGB color space for compatibility with MediaPipe.
+1. **Image Upload**: Users can upload an image of an ASL hand sign.
+2. **Hand Detection**: The app uses MediaPipe to detect hand landmarks in the image.
+3. **Feature Extraction**: Angles between hand landmarks are calculated and normalized.
+4. **Prediction**: A Random Forest model predicts the ASL sign based on the extracted features.
+5. **Visualization**: The app displays the detected hand landmarks and top predictions.
 
-## Hand Landmark Detection
+## Supported Alphabets
 
-We use MediaPipe Hands for detecting hand landmarks in the images:
+The app currently works for the following ASL alphabets:
+A, B, C, E, F, G, H, I, J, K, L, O, Q, R, S, W, Y
 
-1. **MediaPipe Hands**: Initializes with `static_image_mode=True`, `max_num_hands=1`, and `min_detection_confidence=0.5`.
-2. **Landmark Extraction**: 21 hand landmarks are extracted from each image.
+The app does not support or may not work correctly for:
+D, M, N, P, T, U, V, X, Z
 
-## Feature Extraction
+Note: The model's performance may vary and is subject to improvement.
 
-### Landmark Normalization
+## Image Preprocessing and Feature Extraction
 
-1. **Centering**: Landmarks are centered by subtracting the mean position.
-2. **Scaling**: Centered landmarks are scaled to unit variance.
+### Image Preprocessing
+- Image Loading: We use OpenCV (cv2) to load and process images.
+- Color Conversion: Images are converted from BGR to RGB color space for compatibility with MediaPipe.
 
-### Angle Calculation
+### Hand Landmark Detection
+We use MediaPipe Hands for detecting hand landmarks in the images:
+- MediaPipe Hands: Initializes with `static_image_mode=True`, `max_num_hands=1`, and `min_detection_confidence=0.5`.
+- Landmark Extraction: 21 hand landmarks are extracted from each image.
 
-We calculate angles between all pairs of landmarks:
+### Feature Extraction
 
-1. **Vector Calculation**: For each pair of landmarks (i, j), we calculate the vector from i to j.
-2. **Angle Computation**: We compute the arcosine of the x and y components of the normalized vector.
-3. **Feature Vector**: The angles form a 420-dimensional feature vector (21 choose 2 = 210 pairs, 2 angles per pair).
+#### Landmark Normalization
+1. Centering: Landmarks are centered by subtracting the mean position.
+2. Scaling: Centered landmarks are scaled to unit variance.
 
-## Model Input
+#### Angle Calculation
+We calculate angles between all pairs of landmarks:
+1. Vector Calculation: For each pair of landmarks (i, j), we calculate the vector from i to j.
+2. Angle Computation: We compute the arcosine of the x and y components of the normalized vector.
+3. Feature Vector: The angles form a 420-dimensional feature vector (21 choose 2 = 210 pairs, 2 angles per pair).
 
+### Model Input
 The preprocessed features (angles) are used as input to our Random Forest model for ASL sign classification.
 
-This preprocessing pipeline ensures that our model receives consistent and informative features, regardless of the hand's position or size in the original image.
+This preprocessing pipeline ensures that our model receives consistent and informative features, regardless of the hand's position or size in the original image.
+
+## Usage
+
+The "View Hand Landmarks" tab allows users to see hand landmarks for pre-loaded ASL signs. Select one or more alphabets from the supported list to view their corresponding hand landmarks.