Create readme.md

readme.md

@@ -0,0 +1,36 @@
+# ASL Recognition App: Image Preprocessing and Feature Extraction
+
+This document explains the image preprocessing and feature extraction techniques used in our ASL Recognition App.
+
+## Image Preprocessing
+
+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.
+
+## Hand Landmark Detection
+
+We use MediaPipe Hands for detecting hand landmarks in the images:
+
+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.
+
+## Feature Extraction
+
+### Landmark Normalization
+
+1. **Centering**: Landmarks are centered by subtracting the mean position.
+2. **Scaling**: Centered landmarks are scaled to unit variance.
+
+### 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.