2025 黑客松获奖作品：基于OpenVINO与PaddleOCR的输出抄表器

发布时间：2025-07-16

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本文介绍一种无需额外训练的自动化抄表方案，通过人工智能技术解决人工抄表费事、易出错的问题。该方案先配置图片屏幕区域坐标并预处理，再配置待识别元素坐标并裁剪，利用OpenVINO加载PaddleOCR模型识别文字，经结构化输出与后处理得到结果，无需大量贴合业务场景的数据集，适用于内容为文字的规整“表”。

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2023 黑客松获奖作品：基于openvino与paddleocr的输出抄表器 -

背景介绍

“表”是生活中的随处可见的一种设备。常见的“表”包括了家用电表，水表等设备；除此之外，还有工频场强计等“表”。受制于由于受到区域因素以及技术因素的制约，并非每种“表”都能够进行数据的自动采集，从而只能通过人工手动抄表。这种数据采集工作一方面较为费事和枯燥，另一方面，长时间工作带来的会导致工作人员疲劳，从而产生抄录错误。通过人工智能技术构造自动化的抄表流程能够极大的克服上述问题，提高工作效率。

技术方案

本项目提供了有一种无需额外训练的抄表器，只需要人为指定一些和布局有关的配置信息，即可实现表中数据的记录。总体流程如下：

配置图片中屏幕区域的坐标值。（这些坐标值也可以通过cv2的拐点检测或深度学习进行获取）
对图片进行预处理（仿射变换）
配置待识别的元素对应的坐标，并裁剪对应的区域。
如有需要，可以对裁剪下来的区域进行预处理。
基于OpenVINO进行文字识别。
结构化输出信息
如有需要，对输出结果进行进一步精炼。

2023 黑客松获奖作品：基于OpenVINO与PaddleOCR的输出抄表器 -

图片预处理

由于本项目是一个零微调的项目，因此，为了保证识别模型的有效性，需要人工对齐输入信息。

修正倾斜的图片，将图片中的屏幕区域修正到指定的大小
根据从说明书等地方获取到的设备信息，设定待识别的区域在屏幕上的布局。

修正图片

以下列图片为例，本节展示如何将图片从倾斜的状态，修正为正面观众的状态。

2023 黑客松获奖作品：基于OpenVINO与PaddleOCR的输出抄表器 -

In [3]

# 配置坐标信息# The coordinates of the corners of the screen in case 1POINTS = [[1121, 56],    # Left top
          [3242, 183],   # right top
          [3040, 1841],  # right bottom
          [1000, 1543]]   # left bottom# The size of the screen in case 1DESIGN_SHAPE = (1300, 1000)

In [5]

import cv2 
import numpy as np# 定义仿射变换函数def pre_processing(img, point_list, target_shape):
    """
    Preprocessing function for normalizing skewed images.
    Parameters:
        img (np.ndarray): Input image.
        point_list (List[List[int, int], List[int, int], List[int, int]]): Coordinates of the corners of the screen.
        target_shape (Tuple(int, int)): The design shape.
    """
    
    # affine transformations
    # point list is the coordinates of the corners of the screen
    # target shape is the design shape
    
    target_w, target_h = target_shape
    pts1 = np.float32(point_list)
    pts2 = np.float32([[0, 0],[target_w,0],[target_w, target_h],[0,target_h]])
    
    M = cv2.getPerspectiveTransform(pts1, pts2)
    img2 = cv2.warpPerspective(img, M, (target_w,target_h))    return img2

In [6]

import matplotlib.pyplot as plt# 执行预处理img = cv2.imread('example1.jpg')# affine transformations to normalize skewed imagesimg = pre_processing(img, POINTS, DESIGN_SHAPE)# The screen part is cropped and correctedplt.imshow(img)

<matplotlib.image.AxesImage at 0x7fa87a3e9510>

<Figure size 640x480 with 1 Axes>

基于OpenVINO加载PaddleOCR识别模型进行预测

文字识别模型（PaddleOCR）

PaddleOCR 是PaddlePaddle的文字识别套件。迄今为止，PaddleOCR已经提供了许多复用性强的预训练模型。在本项目中使用的预训练模型是Chinese and English ultra-lightweight PP-OCR model (9.4M)。更多的信息请参考PaddleOCR Github或PaddleOCR Gitee。

一个标准的OCR流程包括了文字检测和文字识别，对于本项目来说，文字检测工作已经通过人工配置的方式解决了，因此，只需要进行文字识别即可。

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OpenVINO简介

OpenVINO作为Intel原生的深度学习推理框架，可以最大化的提升人工智能神经网络在Intel平台上的执行性能，实现一次编写，任意部署的开发体验。OpenVINO在2025.1版本后，就可以直接支持飞桨模型，大大提升了模型在Intel异构硬件上的推理性能与部署便捷性，带来更高的生产效率，更广阔的兼容性以及推理性能的优化。

获取模型

In [ ]

! wget "https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar"! tar -xvf ch_PP-OCRv3_rec_infer.tar

基于OpenVINO加载PaddleOCR

使用OpenVINO加载Paddle模型无需经过任何转换，只需要

创建环境
读取模型
生成推理接口

In [ ]

! pip install openvino

In [11]

from openvino.runtime import Core# Initialize OpenVINO Runtime for text recognition.core = Core()# Read the model and corresponding weights from a file.rec_model_file_path = "ch_PP-OCRv3_rec_infer/inference.pdmodel"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)

文字识别

依旧对于上述示例图片，希望结构化输出以下内容：[{"Info_Probe":""}, {"Freq_Set":""}, {"Freq_Main":""}, {"Val_Total":""},{"Val_X":""}, {"Val_Y":""}, {"Val_Z":""}, {"Unit":""}, {"Field":""}]。输出示例如下图所示：

配置布局

首先，需要基于仿射变换的结果，配置各个元素在图片上的布局。这个配置对于同一批表来说是固定的。

In [13]

# features and layout informationDESIGN_LAYOUT = {'Info_Probe':[14, 36, 410, 135],  # feature_name, xmin, ymin, xmax, ymax
                 'Freq_Set':[5, 290, 544, 406], 
                 'Val_Total':[52, 419, 1256, 741], 
                 'Val_X':[19, 774, 433, 882], 
                 'Val_Y':[433, 773, 874, 884], 
                 'Val_Z':[873, 773, 1276, 883], 
                 'Unit':[1064, 291, 1295, 403], 
                 'Field':[5, 913, 243, 998]}

文字识别的预处理函数

In [18]

import copyimport math# Preprocess for text recognition.def resize_norm_img(img, max_wh_ratio):
    """
    Resize input image for text recognition

    Parameters:
        img: image with bounding box from text detection 
        max_wh_ratio: ratio of image scaling
    """
    rec_image_shape = [3, 48, 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    else:
        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_imdef get_rotate_crop_image(img, points):
    '''
    img_height, img_width = img.shape[0:2]
    left = int(np.min(points[:, 0]))
    right = int(np.max(points[:, 0]))
    top = int(np.min(points[:, 1]))
    bottom = int(np.max(points[:, 1]))
    img_crop = img[top:bottom, left:right, :].copy()
    points[:, 0] = points[:, 0] - left
    points[:, 1] = points[:, 1] - top
    '''
    assert len(points) == 4, "shape of points must be 4*2"
    img_crop_width = int(        max(
            np.linalg.norm(points[0] - points[1]),
            np.linalg.norm(points[2] - points[3])))
    img_crop_height = int(        max(
            np.linalg.norm(points[0] - points[3]),
            np.linalg.norm(points[1] - points[2])))
    pts_std = np.float32([[0, 0], [img_crop_width, 0],
                          [img_crop_width, img_crop_height],
                          [0, img_crop_height]])
    M = cv2.getPerspectiveTransform(points, pts_std)
    dst_img = cv2.warpPerspective(
        img,
        M, (img_crop_width, img_crop_height),
        borderMode=cv2.BORDER_REPLICATE,
        flags=cv2.INTER_CUBIC)
    dst_img_height, dst_img_width = dst_img.shape[0:2]    if dst_img_height * 1.0 / dst_img_width >= 1.5:
        dst_img = np.rot90(dst_img)    return dst_imgdef prep_for_rec(dt_boxes, frame):
    """
    Preprocessing of the detected bounding boxes for text recognition

    Parameters:
        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 = get_rotate_crop_image(ori_im, tmp_box)
        img_crop_list.append(img_crop)
        
    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, indicesdef batch_text_box(img_crop_list, img_num, indices, beg_img_no, batch_num):
    """
    Batch for text recognition

    Parameters:
        img_crop_list: processed bounding box images with detected bounding box
        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 in 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.append(norm_img)

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

文字识别的后处理函数

用于将文字识别的结果进行解码，转化为汉字

In [21]

class RecLabelDecode(object):
    """ Convert between text-label and text-index """

    def __init__(self,
                 character_dict_path=None,
                 character_type='ch',
                 use_space_char=False):
        support_character_type = [            'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',            'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',            'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',            'ne', 'EN', 'latin', 'arabic', 'cyrillic', 'devanagari'
        ]        assert character_type in support_character_type, "Only {} are supported now but get {}".format(
            support_character_type, character_type)

        self.beg_str = "sos"
        self.end_str = "eos"

        if character_type == "en":
            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
            dict_character = list(self.character_str)        elif character_type == "EN_symbol":            # same with ASTER setting (use 94 char).
            self.character_str = string.printable[:-6]
            dict_character = list(self.character_str)        elif character_type in support_character_type:
            self.character_str = []            assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
                character_type)            with open(character_dict_path, "rb") as fin:
                lines = fin.readlines()                for line in lines:
                    line = line.decode('utf-8').strip("\n").strip("\r\n")
                    self.character_str.append(line)            if use_space_char:
                self.character_str.append(" ")
            dict_character = list(self.character_str)        else:            raise NotImplementedError
        self.character_type = character_type
        dict_character = self.add_special_char(dict_character)
        self.dict = {}        for i, char in enumerate(dict_character):
            self.dict[char] = i
        self.character = dict_character        
    def __call__(self, preds, label=None, *args, **kwargs):
        preds_idx = preds.argmax(axis=2)
        preds_prob = preds.max(axis=2)
        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)        if label is None:            return text
        label = self.decode(label)        return text, label    
    def add_special_char(self, dict_character):
        dict_character = ['blank'] + dict_character        return dict_character    
    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
        """ convert text-index into text-label. """
        result_list = []
        ignored_tokens = self.get_ignored_tokens()
        batch_size = len(text_index)        for batch_idx in range(batch_size):
            char_list = []
            conf_list = []            for idx in range(len(text_index[batch_idx])):                if text_index[batch_idx][idx] in ignored_tokens:                    continue
                if is_remove_duplicate:                    # only for predict
                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
                            batch_idx][idx]:                        continue
                char_list.append(self.character[int(text_index[batch_idx][
                    idx])])                if text_prob is not None:
                    conf_list.append(text_prob[batch_idx][idx])                else:
                    conf_list.append(1)
            text = ''.join(char_list)
            result_list.append((text, np.mean(conf_list)))        return result_list    
    def get_ignored_tokens(self):
        return [0]  # for ctc blank# Since the recognition results contain chinese words, we should use 'ch' as character_typetext_decoder = RecLabelDecode(character_dict_path="ppocr_keys_v1.txt",
                              character_type='ch',  
                              use_space_char=True)

基于OpenVINO进行文字识别

下面以Freq_Set为例，进行文字识别

In [25]

# 输出结构体struct_result = {} 

# Crop imgs according the layout informationxmin, ymin, xmax, ymax = DESIGN_LAYOUT['Freq_Set']
crop_img = img[ymin:ymax, xmin:xmax]

h = ymax - ymin  # height of crop_imgw = xmax - xmin  # width of crop_imgdt_boxes = [np.array([[0,0],[w,0],[w,h],[0,h]],dtype='float32')]
batch_num = 1# since the input img is cropped, we do not need a detection model to find the position of texts# Preprocess detection results for recognition.img_crop_list, img_num, indices = prep_for_rec(dt_boxes, crop_img)# txts are the recognized text resultsrec_res = [['', 0.0]] * img_num
txts = [] 

for beg_img_no in range(0, img_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.
    rec_result = text_decoder(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))] 

# record the recognition resultstruct_result['Freq_Set'] = txts[0]print(txts[0])

100H2实时值

结构化输出与后处理

上面的逻辑已经完成了使用OpenVINO加载PaddleOCR并进行预测，但实际上由于整个模型没有进行微调，所以对于当前的业务场景来说可能不够完美，这个时候可以通过一些简单的逻辑进行处理，比如，对于示例图片中，H2必然是不存在的，这个地方可以直接通过replace替换为HZ。

简单来说，对于示例图片的这种表，可以定义如下后处理函数：

In [28]

# Post-processing, fix some error made in recognitiondef post_processing(results, post_configration):
    """
    Postprocessing function for correcting the recognition errors.
    Parameters:
        results (Dict): The result directory.
        post_configration (Dict): The configuration directory.
    """
    for key in results.keys():        if len(post_configration[key]) == 0:            continue  # nothing to do
        for post_item in post_configration[key]:
            key_word = post_item[0]            if key_word == 'MP':  # mapping
                source_word = post_item[1]
                target_word = post_item[2]                if source_word in results[key]:
                    results[key] = target_word            elif key_word == 'RP':  # removing
                source_word = post_item[1]
                target_word = post_item[2]
                results[key] = results[key].replace(source_word, target_word)            elif key_word == 'AD':  # add point
                add_position = post_item[1]
                results[key] = results[key][:add_position] + '.' + results[key][add_position:]    return results# 通过配置决定如何进行后处理# Congiguration for postprocessing of the resultsRESULT_POST = {"Info_Probe":[['MP', 'LF', '探头:LF-01']],  # words need to be mapped
               "Freq_Set":[['RP', '实时值', ''], ['RP', ' ', ''], ['RP', 'H2', 'HZ']],  # words need to be replace
               "Val_Total":[['RP', 'H2', 'Hz']],               "Val_X":[['RP', 'X', ''], ['RP', ':', '']], 
               "Val_Y":[['RP', 'Y', ''], ['RP', ':', '']], 
               "Val_Z":[['RP', 'Z', ''], ['RP', ':', '']], 
               "Unit":[['MP', 'T', 'μT'],['MP', 'kV', 'kV/m'],['MP', 'kv', 'kV/m'],['MP', 'vm', 'V/m'],['MP', 'Vm', 'V/m'],['MP', 'A', 'A/m']], 
               "Field":[]}  # nothing need to do# Postprocessing, to fix some error made in recognitionstruct_result = post_processing(struct_result, RESULT_POST)# Print resultprint(struct_result)

{'Freq_Set': '100HZ'}

全流程一键运行

为了方便运行，这里也提供了一个封装好的函数

In [32]

# 为了避免因为图片模糊导致的漏检，配置一个输出模板，从而让每个图片输出格式都一致# Output template in case 1RESULT_TEMP = {"Info_Probe":"探头:---", 
               "Freq_Set":"", 
               "Val_Total":"无探头", 
               "Val_X":"", 
               "Val_Y":"", 
               "Val_Z":"", 
               "Unit":"A/m", 
               "Field":"常规"}

In [33]

# the input of recognition should be image, DESIGN information, compiled_modeldef main_function(img, DESIGN_LAYOUT, RESULT_TEMP, preprocess_function=None):
    """
    Main program of processing the meter.
    Parameters:
        img (np.ndarray): Input image.
        DESIGN_LAYOUT (Dict): The coordinates of elements in the screen.
        RESULT_TEMP (Dict): The template for structure output.
        preprocess_function: The preprocess function for cropped images, `None` means no preprocessing to do.
    """
    # copy the structure output template
    struct_result = copy.deepcopy(RESULT_TEMP)    # structure recognition begins here
    for key in DESIGN_LAYOUT.keys():        # Crop imgs according the layout information
        xmin, ymin, xmax, ymax = DESIGN_LAYOUT[key]
        crop_img = img[ymin:ymax, xmin:xmax]        
        # preprocessing for cropped images
        if preprocess_function is not None:
            crop_img = preprocess_function(crop_img)
        
        h = ymax - ymin  # height of crop_img
        w = xmax - xmin  # width of crop_img
        dt_boxes = [np.array([[0,0],[w,0],[w,h],[0,h]],dtype='float32')]
        batch_num = 1

        # since the input img is cropped, we do not need a detection model to find the position of texts
        # Preprocess detection results for recognition.
        img_crop_list, img_num, indices = prep_for_rec(dt_boxes, crop_img)        # txts are the recognized text results
        rec_res = [['', 0.0]] * img_num
        txts = [] 

        for beg_img_no in range(0, img_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.
            rec_result = text_decoder(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))] 

        # record the recognition result
        struct_result[key] = txts[0]        
    return struct_result

In [34]

img = cv2.imread('example1.jpg')# affine transformations to normalize skewed imagesimg = pre_processing(img, POINTS, DESIGN_SHAPE)

struct_result = main_function(img, DESIGN_LAYOUT, RESULT_TEMP)# Postprocessing, to fix some error made in recognitionstruct_result = post_processing(struct_result, RESULT_POST)# Print resultprint(struct_result)

{'Info_Probe': '探头:LF-01', 'Freq_Set': '100HZ', 'Val_Total': '734.57', 'Val_X': '734.53', 'Val_Y': '5.05', 'Val_Z': '5.48', 'Unit': 'V/m', 'Field': '电场'}

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