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app.py

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+import gradio as gr
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+from transformers import TrOCRProcessor
+from transformers import VisionEncoderDecoderModel
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import warnings
+
+warnings.filterwarnings("ignore")
+
+processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
+
+def extract_text(image):
+  # calling the processor is equivalent to calling the feature extractor
+  pixel_values = processor(image, return_tensors="pt").pixel_values
+  generated_ids = model.generate(pixel_values)
+  generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+  return generated_text
+
+def hand_written(image_raw):
+  image_raw = np.array(image_raw)
+  image = cv2.cvtColor(image_raw,cv2.COLOR_BGR2GRAY)
+  image = cv2.GaussianBlur(image,(5,5),0)
+  image = cv2.threshold(image,200,255,cv2.THRESH_BINARY_INV)[1]
+  kernal = cv2.getStructuringElement(cv2.MORPH_RECT,(10,1))
+  image = cv2.dilate(image,kernal,iterations=5)
+  contours,hier = cv2.findContours(image,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
+  all_box = []
+  for i in contours:
+    bbox = cv2.boundingRect(i)
+    all_box.append(bbox)
+
+  # Calculate maximum rectangle height
+  c = np.array(all_box)
+  max_height = np.max(c[::, 3])
+
+  # Sort the contours by y-value
+  by_y = sorted(all_box, key=lambda x: x[1])  # y values
+
+  line_y = by_y[0][1]       # first y
+  line = 1
+  by_line = []
+
+  # Assign a line number to each contour
+  for x, y, w, h in by_y:
+      if y > line_y + max_height:
+          line_y = y
+          line += 1          
+      by_line.append((line, x, y, w, h))
+
+  # This will now sort automatically by line then by x
+  contours_sorted = [(x, y, w, h) for line, x, y, w, h in sorted(by_line)]
+
+  text = ""
+
+  for line in contours_sorted:
+    x,y,w,h = line
+    cropped_image = image_raw[y:y+h,x:x+w]
+    try:
+      extracted = extract_text(cropped_image)
+      if not extracted == "0 0" and not extracted == "0 1":
+        text = "\n".join([text,extracted])
+    except:
+      print("skiping")
+      pass
+  return text
+
+# load image examples from the IAM database
+
+title = "TrOCR + EN_ICR demo"
+description = "TrOCR Handwritten Recognizer"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models</a> | <a href='https://github.com/microsoft/unilm/tree/master/trocr'>Github Repo</a></p>"
+examples =[["image_0.png"]]
+
+iface = gr.Interface(fn=hand_written, 
+                     inputs=gr.inputs.Image(type="pil"), 
+                     outputs=gr.outputs.Textbox(),
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples)
+
+iface.launch(debug=True,share=True)