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app.py

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+import pandas as pd
+import streamlit as st
+import skimage.io as io
+from PIL import Image
+import numpy as np 
+import matplotlib.pyplot as plt 
+import seaborn as sns 
+import cv2 
+from skimage.filters import threshold_local
+import pytesseract
+import re
+
+from pytesseract import Output
+
+def plot_gray(image):
+    plt.figure(figsize=(16,10))
+    return plt.imshow(image, cmap='Greys_r')
+
+def plot_rgb(image):
+    plt.figure(figsize=(16,10))
+    return plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+def bw_scanner(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    T = threshold_local(gray, 21, offset = 5, method = "gaussian")
+    return (gray > T).astype("uint8") * 255
+
+def text_box_detection(image):
+  d = pytesseract.image_to_data(image, output_type=Output.DICT)
+  n_boxes = len(d['level'])
+  boxes = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB)
+  for i in range(n_boxes):
+      (x, y, w, h) = (d['left'][i], d['top'][i], d['width'][i], d['height'][i])    
+      boxes = cv2.rectangle(boxes, (x, y), (x + w, y + h), (0, 255, 0), 2)
+      
+  return boxes
+
+def ui(): 
+    st.markdown("# Text Extraction")
+    uploaded_file = st.file_uploader("Upload an Image", type=['png', 'jpeg', 'jpg'])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        img_array = np.array(image)
+        gray_image=bw_scanner(img_array)
+        boxes=text_box_detection(gray_image)
+        st.image(boxes, width = 500, channels = 'RGB')
+        extracted_text = pytesseract.image_to_string(img_array)
+        st.markdown(f"Predicted Text {extracted_text}") 
+
+if __name__ == '__main__':
+    ui()
+
+
+        
+

requirements.txt

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+pandas 
+streamlit 
+scikit-image
+pillow 
+numpy 
+matplotlib 
+seaborn 
+opencv-python 
+pytesseract
+apt install tesseract-ocr