| | 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: |
| | |
| | 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() |
| |
|
| | |
| | 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: |
| | 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() |
