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import streamlit as st
import easyocr
import cv2
import numpy as np
from PIL import Image
import tempfile
import os
st.set_page_config(page_title="🧾 EasyOCR Image Text Extractor", layout="centered")
st.title("🧾 Image OCR with EasyOCR")
# Upload image file
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
def process_image(image_path, gpu=False):
reader = easyocr.Reader(['en'], gpu=gpu)
result = reader.readtext(image_path)
text_lines = [detection[1] for detection in result]
saved_text = " ".join(text_lines)
img = cv2.imread(image_path)
for detection in result:
top_left = tuple([int(val) for val in detection[0][0]])
bottom_right = tuple([int(val) for val in detection[0][2]])
text = detection[1]
font = cv2.FONT_HERSHEY_SIMPLEX
img = cv2.rectangle(img, top_left, bottom_right, (0, 255, 0), 2)
img = cv2.putText(img, text, top_left, font, 0.6, (255, 255, 255), 1, cv2.LINE_AA)
return img, saved_text
if uploaded_file:
st.success("✅ Image uploaded successfully!")
# Save the uploaded file temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
temp_file.write(uploaded_file.read())
temp_path = temp_file.name
with st.spinner("🔍 Running OCR..."):
# Process image
img_result, extracted_text = process_image(temp_path)
st.success("🎉 OCR complete!")
# Show image with bounding boxes
st.image(cv2.cvtColor(img_result, cv2.COLOR_BGR2RGB), caption="Detected Text", use_column_width=True)
# Show extracted text
st.subheader("📄 Extracted Text")
st.text_area("Detected Text", extracted_text, height=200)
# Option to download text
st.download_button("Download Extracted Text", extracted_text, file_name="extracted_text.txt")
# Clean up temporary file
os.remove(temp_path)
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