code: def draw_ocr(
      image,
      boxes,
      txts=None,
      scores=None,
      drop_score=0.5,
      font_path="./doc/fonts/simfang.ttf",
  ):
      """
      Visualize the results of OCR detection and recognition
      args:
          image(Image|array): RGB image
          boxes(list): boxes with shape(N, 4, 2)
          txts(list): the texts
          scores(list): txxs corresponding scores
          drop_score(float): only scores greater than drop_threshold will be visualized
          font_path: the path of font which is used to draw text
      return(array):
          the visualized img
      """
      if scores is None:
          scores = [1] * len(boxes)
      box_num = len(boxes)
      for i in range(box_num):
          if scores is not None and (
              scores[i] < drop_score or math.isnan(scores[i])
          ):
              continue
          box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(
              np.int64
          )
          image = cv2.polylines(
              np.array(image), [box], True, (255, 0, 0), 2
          )
      if txts is not None:
          img = np.array(resize_img(image, input_size=600))
          txt_img = text_visual(
              txts,
              scores,
              img_h=img.shape[0],
              img_w=600,
              threshold=drop_score,
              font_path=font_path,
          )
          img = np.concatenate(
              [np.array(img), np.array(txt_img)], axis=1
          )
          return img
      return image
  
imports: import argparse
  import os
  import sys
  import platform
  import cv2
  import numpy
  import paddle
  from PIL import Image, ImageDraw, ImageFont
  import math
  from paddle import inference
  import time
  import ast
label_desc: Use image node_id 7 for calling a global function with example usage: draw_ocr(image, boxes, txts, scores, drop_score, font_path) and returns: image, img