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app.py

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+import gradio as gr
+import easyocr
+import cv2
+import numpy as np
+from PIL import Image
+
+# Create an EasyOCR Reader
+reader = easyocr.Reader(['en'])
+
+def process_image(image):
+    # Convert the PIL image to a numpy array (compatible with OpenCV)
+    image_np = np.array(image)
+
+    # Convert the image to RGB (OpenCV loads as BGR, EasyOCR expects RGB)
+    image_rgb = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
+
+    # Use EasyOCR to read text from the image
+    result = reader.readtext(image_rgb)
+
+    # Draw bounding boxes around detected text
+    for (bbox, text, prob) in result:
+        (top_left, top_right, bottom_right, bottom_left) = bbox
+        top_left = tuple(map(int, top_left))
+        bottom_right = tuple(map(int, bottom_right))
+        cv2.rectangle(image_np, top_left, bottom_right, (0, 255, 0), 2)
+    
+    # Convert back to RGB for display
+    result_image = Image.fromarray(cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB))
+    
+    # Combine detected text and their confidence scores
+    detected_text = "\n".join([f"Detected text: {text}, Confidence: {prob:.2f}" for (_, text, prob) in result])
+    
+    return result_image, detected_text
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=process_image, 
+    inputs="image", 
+    outputs=["image", "text"], 
+    title="OCR with EasyOCR", 
+    description="Upload an image, and the system will detect text using EasyOCR and display it."
+)
+
+# Launch the interface
+interface.launch()

requirements.txt

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+easyocr
+opencv-python-headless
+numpy
+pillow
+gradio