PaddleOCR with OpenVINO™

This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS.


This demo shows how to run PP-OCR model on OpenVINO natively. Instead of exporting the PaddlePaddle model to ONNX and then converting to the OpenVINO Intermediate Representation (OpenVINO IR) format with Model Optimizer, you can now read directly from the PaddlePaddle Model without any conversions. PaddleOCR is an ultra-light OCR model trained with PaddlePaddle deep learning framework, that aims to create multilingual and practical OCR tools.

The PaddleOCR pre-trained model used in the demo refers to the “Chinese and English ultra-lightweight PP-OCR model (9.4M)”. More open source pre-trained models can be downloaded at PaddleOCR Github or PaddleOCR Gitee. Working pipeline of the PaddleOCR is as follows:

NOTE: To use this notebook with a webcam, you need to run the notebook on a computer with a webcam. If you run the notebook on a server, the webcam will not work. You can still do inference on a video file.


import sys
import os
import cv2
import numpy as np
import paddle
import math
import time
import collections
from PIL import Image
from pathlib import Path
import tarfile
import urllib.request

from openvino.runtime import Core
from IPython import display
import copy

import notebook_utils as utils
import pre_post_processing as processing
/opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-231/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/paddle/vision/transforms/ DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
  'nearest': Image.NEAREST,
/opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-231/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/paddle/vision/transforms/ DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
  'bilinear': Image.BILINEAR,
/opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-231/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/paddle/vision/transforms/ DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
  'bicubic': Image.BICUBIC,
/opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-231/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/paddle/vision/transforms/ DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
  'box': Image.BOX,
/opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-231/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/paddle/vision/transforms/ DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
  'lanczos': Image.LANCZOS,
/opt/home/k8sworker/cibuilds/ov-notebook/OVNotebookOps-231/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/paddle/vision/transforms/ DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
  'hamming': Image.HAMMING

Models for PaddleOCR

PaddleOCR includes two parts of deep learning models, text detection and text recognition. Pre-trained models used in the demo are downloaded and stored in the “model” folder.

Only a few lines of code are required to run the model. First, initialize the runtime for inference. Then, read the network architecture and model weights from the .pdmodel and .pdiparams files to load to CPU.

# Define the function to download text detection and recognition models from PaddleOCR resources.

def run_model_download(model_url, model_file_path):
    Download pre-trained models from PaddleOCR resources

        model_url: url link to pre-trained models
        model_file_path: file path to store the downloaded model
    model_name = model_url.split("/")[-1]

    if model_file_path.is_file():
        print("Model already exists")
        # Download the model from the server, and untar it.
        print("Downloading the pre-trained model... May take a while...")

        # Create a directory.
        os.makedirs("model", exist_ok=True)
        urllib.request.urlretrieve(model_url, f"model/{model_name} ")
        print("Model Downloaded")

        file ="model/{model_name} ")
        res = file.extractall("model")
        if not res:
            print(f"Model Extracted to {model_file_path}.")
            print("Error Extracting the model. Please check the network.")

Download the Model for Text Detection

# A directory where the model will be downloaded.

det_model_url = ""
det_model_file_path = Path("model/ch_ppocr_mobile_v2.0_det_infer/inference.pdmodel")

run_model_download(det_model_url, det_model_file_path)
Downloading the pre-trained model... May take a while...
Model Downloaded
Model Extracted to model/ch_ppocr_mobile_v2.0_det_infer/inference.pdmodel.

Load the Model for Text Detection

# Initialize OpenVINO Runtime for text detection.
core = Core()
det_model = core.read_model(model=det_model_file_path)
det_compiled_model = core.compile_model(model=det_model, device_name="CPU")

# Get input and output nodes for text detection.
det_input_layer = det_compiled_model.input(0)
det_output_layer = det_compiled_model.output(0)

Download the Model for Text Recognition

rec_model_url = ""
rec_model_file_path = Path("model/ch_ppocr_mobile_v2.0_rec_infer/inference.pdmodel")

run_model_download(rec_model_url, rec_model_file_path)
Downloading the pre-trained model... May take a while...
Model Downloaded
Model Extracted to model/ch_ppocr_mobile_v2.0_rec_infer/inference.pdmodel.

Load the Model for Text Recognition with Dynamic Shape

Input to text recognition model refers to detected bounding boxes with different image sizes, for example, dynamic input shapes. Hence:

  1. Input dimension with dynamic input shapes needs to be specified before loading text recognition model.

  2. Dynamic shape is specified by assigning -1 to the input dimension or by setting the upper bound of the input dimension using, for example, Dimension(1, 512).Note: Since the text recognition model is with dynamic input shape and current release of OpenVINO 2022.1 does not support dynamic shape on iGPU, you cannot directly switch device to iGPU for inference in this case. Otherwise, you may need to resize the input images to this model into a fixed size and then try running the inference on iGPU.

# Read the model and corresponding weights from a file.
rec_model = core.read_model(model=rec_model_file_path)

# Assign dynamic shapes to every input layer on the last dimension.
for input_layer in rec_model.inputs:
    input_shape = input_layer.partial_shape
    input_shape[3] = -1
    rec_model.reshape({input_layer: input_shape})

rec_compiled_model = core.compile_model(model=rec_model, device_name="CPU")

# Get input and output nodes.
rec_input_layer = rec_compiled_model.input(0)
rec_output_layer = rec_compiled_model.output(0)

Preprocessing Image Functions for Text Detection and Recognition

Define preprosessing functions for text detection and recognition: 1. Preprocessing for text detection: resize and normalize input images. 2. Preprocessing for text recognition: resize and normalize detected box images to the same size (for example, (3, 32, 320) size for images with Chinese text) for easy batching in inference.

# Preprocess for text detection.
def image_preprocess(input_image, size):
    Preprocess input image for text detection

        input_image: input image
        size: value for the image to be resized for text detection model
    img = cv2.resize(input_image, (size, size))
    img = np.transpose(img, [2, 0, 1]) / 255
    img = np.expand_dims(img, 0)
    # NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
    img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
    img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
    img -= img_mean
    img /= img_std
    return img.astype(np.float32)
# Preprocess for text recognition.
def resize_norm_img(img, max_wh_ratio):
    Resize input image for text recognition

        img: bounding box image from text detection
        max_wh_ratio: value for the resizing for text recognition model
    rec_image_shape = [3, 32, 320]
    imgC, imgH, imgW = rec_image_shape
    assert imgC == img.shape[2]
    character_type = "ch"
    if character_type == "ch":
        imgW = int((32 * max_wh_ratio))
    h, w = img.shape[:2]
    ratio = w / float(h)
    if math.ceil(imgH * ratio) > imgW:
        resized_w = imgW
        resized_w = int(math.ceil(imgH * ratio))
    resized_image = cv2.resize(img, (resized_w, imgH))
    resized_image = resized_image.astype('float32')
    resized_image = resized_image.transpose((2, 0, 1)) / 255
    resized_image -= 0.5
    resized_image /= 0.5
    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
    padding_im[:, :, 0:resized_w] = resized_image
    return padding_im

def prep_for_rec(dt_boxes, frame):
    Preprocessing of the detected bounding boxes for text recognition

        dt_boxes: detected bounding boxes from text detection
        frame: original input frame
    ori_im = frame.copy()
    img_crop_list = []
    for bno in range(len(dt_boxes)):
        tmp_box = copy.deepcopy(dt_boxes[bno])
        img_crop = processing.get_rotate_crop_image(ori_im, tmp_box)

    img_num = len(img_crop_list)
    # Calculate the aspect ratio of all text bars.
    width_list = []
    for img in img_crop_list:
        width_list.append(img.shape[1] / float(img.shape[0]))

    # Sorting can speed up the recognition process.
    indices = np.argsort(np.array(width_list))
    return img_crop_list, img_num, indices

def batch_text_box(img_crop_list, img_num, indices, beg_img_no, batch_num):
    Batch for text recognition

        img_crop_list: processed detected bounding box images
        img_num: number of bounding boxes from text detection
        indices: sorting for bounding boxes to speed up text recognition
        beg_img_no: the beginning number of bounding boxes for each batch of text recognition inference
        batch_num: number of images for each batch
    norm_img_batch = []
    max_wh_ratio = 0
    end_img_no = min(img_num, beg_img_no + batch_num)
    for ino in range(beg_img_no, end_img_no):
        h, w = img_crop_list[indices[ino]].shape[0:2]
        wh_ratio = w * 1.0 / h
        max_wh_ratio = max(max_wh_ratio, wh_ratio)
    for ino in range(beg_img_no, end_img_no):
        norm_img = resize_norm_img(img_crop_list[indices[ino]], max_wh_ratio)
        norm_img = norm_img[np.newaxis, :]

    norm_img_batch = np.concatenate(norm_img_batch)
    norm_img_batch = norm_img_batch.copy()
    return norm_img_batch

Postprocessing Image for Text Detection

def post_processing_detection(frame, det_results):
    Postprocess the results from text detection into bounding boxes

        frame: input image
        det_results: inference results from text detection model
    ori_im = frame.copy()
    data = {'image': frame}
    data_resize = processing.DetResizeForTest(data)
    data_list = []
    keep_keys = ['image', 'shape']
    for key in keep_keys:
    img, shape_list = data_list

    shape_list = np.expand_dims(shape_list, axis=0)
    pred = det_results[0]
    if isinstance(pred, paddle.Tensor):
        pred = pred.numpy()
    segmentation = pred > 0.3

    boxes_batch = []
    for batch_index in range(pred.shape[0]):
        src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
        mask = segmentation[batch_index]
        boxes, scores = processing.boxes_from_bitmap(pred[batch_index], mask, src_w, src_h)
        boxes_batch.append({'points': boxes})
    post_result = boxes_batch
    dt_boxes = post_result[0]['points']
    dt_boxes = processing.filter_tag_det_res(dt_boxes, ori_im.shape)
    return dt_boxes

Main Processing Function for PaddleOCR

Run paddleOCR function in different operations, either a webcam or a video file. See the list of procedures below:

  1. Create a video player to play with target fps (utils.VideoPlayer).

  2. Prepare a set of frames for text detection and recognition.

  3. Run AI inference for both text detection and recognition.

  4. Visualize the results.

def run_paddle_ocr(source=0, flip=False, use_popup=False, skip_first_frames=0):
    Main function to run the paddleOCR inference:
    1. Create a video player to play with target fps (utils.VideoPlayer).
    2. Prepare a set of frames for text detection and recognition.
    3. Run AI inference for both text detection and recognition.
    4. Visualize the results.

        source: The webcam number to feed the video stream with primary webcam set to "0", or the video path.
        flip: To be used by VideoPlayer function for flipping capture image.
        use_popup: False for showing encoded frames over this notebook, True for creating a popup window.
        skip_first_frames: Number of frames to skip at the beginning of the video.
    # Create a video player to play with target fps.
    player = None
        player = utils.VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
        # Start video capturing.
        if use_popup:
            title = "Press ESC to Exit"
            cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)

        processing_times = collections.deque()
        while True:
            # Grab the frame.
            frame =
            if frame is None:
                print("Source ended")
            # If the frame is larger than full HD, reduce size to improve the performance.
            scale = 1280 / max(frame.shape)
            if scale < 1:
                frame = cv2.resize(src=frame, dsize=None, fx=scale, fy=scale,
            # Preprocess the image for text detection.
            test_image = image_preprocess(frame, 640)

            # Measure processing time for text detection.
            start_time = time.time()
            # Perform the inference step.
            det_results = det_compiled_model([test_image])[det_output_layer]
            stop_time = time.time()

            # Postprocessing for Paddle Detection.
            dt_boxes = post_processing_detection(frame, det_results)

            processing_times.append(stop_time - start_time)
            # Use processing times from last 200 frames.
            if len(processing_times) > 200:
            processing_time_det = np.mean(processing_times) * 1000

            # Preprocess detection results for recognition.
            dt_boxes = processing.sorted_boxes(dt_boxes)
            batch_num = 6
            img_crop_list, img_num, indices = prep_for_rec(dt_boxes, frame)

            # For storing recognition results, include two parts:
            # txts are the recognized text results, scores are the recognition confidence level.
            rec_res = [['', 0.0]] * img_num
            txts = []
            scores = []

            for beg_img_no in range(0, img_num, batch_num):

                # Recognition starts from here.
                norm_img_batch = batch_text_box(
                    img_crop_list, img_num, indices, beg_img_no, batch_num)

                # Run inference for text recognition.
                rec_results = rec_compiled_model([norm_img_batch])[rec_output_layer]

                # Postprocessing recognition results.
                postprocess_op = processing.build_post_process(processing.postprocess_params)
                rec_result = postprocess_op(rec_results)
                for rno in range(len(rec_result)):
                    rec_res[indices[beg_img_no + rno]] = rec_result[rno]
                if rec_res:
                    txts = [rec_res[i][0] for i in range(len(rec_res))]
                    scores = [rec_res[i][1] for i in range(len(rec_res))]

            image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            boxes = dt_boxes
            # Draw text recognition results beside the image.
            draw_img = processing.draw_ocr_box_txt(

            # Visualize the PaddleOCR results.
            f_height, f_width = draw_img.shape[:2]
            fps = 1000 / processing_time_det
            cv2.putText(img=draw_img, text=f"Inference time: {processing_time_det:.1f}ms ({fps:.1f} FPS)",
                        org=(20, 40),fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=f_width / 1000,
                        color=(0, 0, 255), thickness=1, lineType=cv2.LINE_AA)

            # Use this workaround if there is flickering.
            if use_popup:
                draw_img = cv2.cvtColor(draw_img, cv2.COLOR_RGB2BGR)
                cv2.imshow(winname=title, mat=draw_img)
                key = cv2.waitKey(1)
                # escape = 27
                if key == 27:
                # Encode numpy array to jpg.
                draw_img = cv2.cvtColor(draw_img, cv2.COLOR_RGB2BGR)
                _, encoded_img = cv2.imencode(ext=".jpg", img=draw_img,
                                              params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                # Create an IPython image.
                i = display.Image(data=encoded_img)
                # Display the image in this notebook.

    # ctrl-c
    except KeyboardInterrupt:
    # any different error
    except RuntimeError as e:
        if player is not None:
            # Stop capturing.
        if use_popup:

Run Live PaddleOCR with OpenVINO

Use a webcam as the video input. By default, the primary webcam is set with source=0. If you have multiple webcams, each one will be assigned a consecutive number starting at 0. Set flip=True when using a front-facing camera. Some web browsers, especially Mozilla Firefox, may cause flickering. If you experience flickering, set use_popup=True.

NOTE: Popup mode may not work if you run this notebook on a remote computer.

Run live PaddleOCR:

run_paddle_ocr(source=0, flip=False, use_popup=False)
Cannot open camera 0

If you do not have a webcam, you can still run this demo with a video file. Any format supported by OpenCV will work.

# Test OCR results on a video file.

video_file = ""
run_paddle_ocr(source=video_file, flip=False, use_popup=False, skip_first_frames=0)
Source ended