Create app.py

app.py

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+import streamlit as st
+import pytesseract
+import cv2
+import pdf2image
+import numpy as np
+import layoutparser as lp
+from PIL import Image
+
+
+def text_detection(img):
+  res = Image.open(img)
+  res = np.array(res)
+  overlay = Image.open(img)
+  overlay = np.array(overlay)
+  with st.spinner('Processing Text Detection'):
+    boxes = pytesseract.image_to_data(res)
+    for i,b in enumerate(boxes.splitlines()): 
+      if i!=0:
+        b = b.split()
+        if len(b)==12:
+          x,y,w,h = int(b[6]),int(b[7]),int(b[8]),int(b[9])
+          cv2.rectangle(overlay,(x-5,y-5),(w+x+5,h+y+5),(185,237,221),-1) 
+          #res = cv2.putText(res,b[11],(x,y),cv2.FONT_HERSHEY_COMPLEX,1,(185,237,221),2)
+  new = cv2.addWeighted(overlay, 0.4, res, 1 - 0.4, 0)
+  st.subheader("Text Detection")
+  st.image(new)
+
+def parserIMG(path):
+  res = Image.open(path)
+  with st.spinner('Processing Layout Analysis'):
+    model = lp.Detectron2LayoutModel('lp://PubLayNet/faster_rcnn_R_50_FPN_3x/config',
+                                  extra_config=["MODEL.ROI_HEADS.SCORE_THRESH_TEST", 0.5],
+                                  label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"})
+    layout_result = model.detect(res)
+    res=lp.draw_box(res, layout_result,  box_width=5, box_alpha=0.2, show_element_type=True)
+  st.subheader("Layout Analysis")
+  st.image(res)
+
+def parserPDF(path):
+  img = np.asarray(pdf2image.convert_from_bytes(path.read()))
+  st.subheader("Layout Analysis")
+
+  with st.spinner('Processing Layout Analysis'):
+    model = lp.Detectron2LayoutModel('lp://PubLayNet/faster_rcnn_R_50_FPN_3x/config',
+                                  extra_config=["MODEL.ROI_HEADS.SCORE_THRESH_TEST", 0.5],
+                                  label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"})
+    for i in img:
+      layout_result = model.detect(i)
+      i=lp.draw_box(i, layout_result,  box_width=5, box_alpha=0.2, show_element_type=True)
+      st.image(i)
+
+#title 
+st.title('Text Detection & Layout Analysis')
+#uploading file
+file = st.file_uploader(label='Upload your document here',type=['png','jpg','jpeg','pdf'])
+button = st.button('Confirm')
+if button and file is not None:
+    if file.type=="image/png" or file.type=="image/jpg" or file.type=="image/jpeg":
+      parserIMG(file)
+      text_detection(file)
+    elif file.type == "application/pdf":
+      parserPDF(file)