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