Create app.py

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+import easyocr
+import re
+from langdetect import detect_langs
+from PIL import Image
+import io
+
+def load_easyocr_reader():
+    return easyocr.Reader(['hi', 'en'], gpu=False)
+
+def preprocess_image(image):
+    img_array = np.array(image.convert('RGB'))
+    gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+    denoised = cv2.fastNlMeansDenoising(gray, None, 10, 7, 21)
+    thresh = cv2.adaptiveThreshold(denoised, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
+    kernel = np.ones((1, 1), np.uint8)
+    dilated = cv2.dilate(thresh, kernel, iterations=1)
+    return dilated
+
+def perform_easyocr(image, reader):
+    preprocessed_image = preprocess_image(image)
+    results = reader.readtext(preprocessed_image, paragraph=True, detail=0, 
+                              contrast_ths=0.2, adjust_contrast=0.5, 
+                              add_margin=0.1, width_ths=0.7, height_ths=0.7)
+    extracted_text = ' '.join(results)
+    return extracted_text
+
+def detect_languages(text):
+    cleaned_text = re.sub(r'[^a-zA-Z\u0900-\u097F\s]', '', text)
+    cleaned_text = re.sub(r'\s+', ' ', cleaned_text).strip()
+    
+    if not cleaned_text:
+        return []
+    try:
+        langs = detect_langs(cleaned_text)
+        detected = []
+        for lang in langs:
+            if lang.lang == 'hi' and lang.prob > 0.1:
+                detected.append('Hindi')
+            elif lang.lang == 'en' and lang.prob > 0.1:
+                detected.append('English')
+        return detected
+    except:
+        return fallback_language_check(cleaned_text)
+
+def fallback_language_check(text):
+    hindi_range = range(0x0900, 0x097F)
+    english_range = range(0x0041, 0x007A)
+    
+    has_hindi = any(ord(char) in hindi_range for char in text)
+    has_english = any(ord(char) in english_range for char in text)
+    
+    detected = []
+    if has_hindi:
+        detected.append('Hindi')
+    if has_english:
+        detected.append('English')
+    
+    return detected
+
+def highlight_text(text, keywords):
+    for keyword in keywords:
+        # Highlight the keyword by wrapping it with a span tag
+        text = re.sub(f'({re.escape(keyword)})', r'<span style="background-color: yellow;">\1</span>', text, flags=re.IGNORECASE)
+    return text
+
+def main():
+    st.title("OCR for Hindi and English")
+
+    easyocr_reader = load_easyocr_reader()
+
+    # Initialize session state variables
+    if 'extracted_text' not in st.session_state:
+        st.session_state.extracted_text = ""
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+        if st.button('Perform OCR'):
+            with st.spinner('Processing...'):
+                st.session_state.extracted_text = perform_easyocr(image, easyocr_reader)
+                
+                st.subheader("Extracted Text:")
+                st.write(st.session_state.extracted_text)
+                
+                languages_detected = detect_languages(st.session_state.extracted_text)
+                if languages_detected:
+                    st.write("Detected languages:", ', '.join(languages_detected))
+                else:
+                    st.write("No languages detected.")
+
+        if st.session_state.extracted_text:  # Check if OCR has been performed
+            st.subheader("Search in Extracted Text")
+            search_query = st.text_input("Enter keywords to search:", "")
+            if search_query:
+                keywords = search_query.split()
+                highlighted_text = highlight_text(st.session_state.extracted_text, keywords)
+                st.markdown(highlighted_text, unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()