first Version

appeasy.py

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+import gradio as gr
+import easyocr
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import os
+import requests
+from pathlib import Path
+
+# Download and cache the font file
+def get_font():
+    font_path = Path("Roboto-Regular.ttf")
+    if not font_path.exists():
+        font_url = "https://github.com/google/fonts/raw/main/apache/roboto/Roboto-Regular.ttf"
+        response = requests.get(font_url)
+        font_path.write_bytes(response.content)
+    return str(font_path)
+
+# Initialize EasyOCR Reader for Arabic and English
+reader = easyocr.Reader(['ar', 'en'], gpu=True)  # Set gpu=False if no GPU available
+
+def ocr_extract_text(image):
+    if image is None:
+        return "No image provided", None
+    
+    # Convert to RGB if needed
+    if len(image.shape) == 3 and image.shape[2] == 4:  # RGBA
+        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
+    
+    # Perform OCR
+    results = reader.readtext(image)
+    
+    if not results:
+        return "No text detected in the image", image
+    
+    # Prepare text output and confidence scores
+    detected_text = []
+    for (_, text, confidence) in results:
+        detected_text.append(f"{text} (Confidence: {confidence:.2f})")
+    
+    # Create annotated image
+    pil_image = Image.fromarray(image)
+    draw = ImageDraw.Draw(pil_image)
+    
+    # Get font for annotation
+    try:
+        font = ImageFont.truetype(get_font(), size=20)
+    except Exception as e:
+        print(f"Error loading font: {e}")
+        font = ImageFont.load_default()
+    
+    # Draw boxes and text
+    for (bbox, text, confidence) in results:
+        # Convert points to integers
+        top_left = tuple(map(int, bbox[0]))
+        bottom_right = tuple(map(int, bbox[2]))
+        
+        # Draw rectangle
+        draw.rectangle([top_left, bottom_right], outline="red", width=3)
+        
+        # Draw text with confidence
+        text_with_conf = f"{text} ({confidence:.2f})"
+        draw.text(top_left, text_with_conf, fill="blue", font=font)
+    
+    # Convert back to numpy array
+    annotated_image = np.array(pil_image)
+    
+    # Join detected text with proper formatting
+    text_output = "\n".join(detected_text)
+    
+    return text_output, annotated_image
+
+# Custom CSS for RTL support and better text display
+css = """
+.output-text { 
+    direction: rtl; 
+    text-align: right; 
+    font-family: Arial, sans-serif;
+    white-space: pre-wrap;
+}
+"""
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=ocr_extract_text,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=[
+        gr.Textbox(
+            label="Extracted Text (Arabic & English)", 
+            elem_classes=["output-text"]
+        ),
+        gr.Image(label="Annotated Image")
+    ],
+    title="Arabic & English OCR Extractor",
+    description="Upload an image containing Arabic and/or English text for OCR processing. The system will detect and extract text in both languages.",
+    css=css,
+    examples=[],  # You can add example images here
+    cache_examples=True
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch(debug=True, share=True)