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app.py

@@ -0,0 +1,46 @@
+import streamlit as st
+import easyocr
+import numpy as np
+from PIL import Image
+import cv2
+
+st.set_page_config(page_title="OCR Text Extractor", layout="centered")
+st.title("🧠 Image to Text Detection (OCR)")
+st.write("Upload an image to extract text more accurately!")
+
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+def preprocess_image(image):
+    # Convert to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    
+    # Resize image to double the size (helps OCR sometimes)
+    scale_percent = 200
+    width = int(gray.shape[1] * scale_percent / 100)
+    height = int(gray.shape[0] * scale_percent / 100)
+    resized = cv2.resize(gray, (width, height), interpolation=cv2.INTER_LINEAR)
+    
+    # Apply thresholding (binary black & white)
+    _, thresh = cv2.threshold(resized, 150, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    
+    return thresh
+
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Original Image", use_container_width=True)
+
+    img_np = np.array(image)
+    processed_img = preprocess_image(img_np)
+
+    st.image(processed_img, caption="Preprocessed Image", use_container_width=True, channels="GRAY")
+
+    # OCR
+    reader = easyocr.Reader(['en'], gpu=False)
+    results = reader.readtext(processed_img)
+
+    st.subheader("📃 Extracted Text:")
+    if results:
+        for bbox, text, prob in results:
+            st.write(f"- {text}")
+    else:
+        st.warning("❌ No text found.")

requirements.txt

@@ -1,3 +1,4 @@
-altair
-pandas
-streamlit
+streamlit
+easyocr
+opencv-python-headless
+pillow