转自AI Studio，原文链接：​​​​​​[PaddleOCR]基于PaddleX字体分类的OCR输出优化 - 飞桨AI Studio

字体分类

本项目是PaddleOCR的其中一个环节。

目的是达到对PaddleOCR数据中图片的文字字体类别的识别。

0 汉字图片数据获取(可选)

需要增加字体类别时候再看此大步骤

本项目中以及放置了生成好的图片文件，不需要再执行此步骤,但下面的解压还是需要的，会用到里面的字体库

In [ ] 
!unzip /home/aistudio/data/data122617/Createfont.zip -d work

记得自行修改两个文件中涉及到路径的内容，解压前的数据集的路径并不是正确的。

在work/Createfont/Word_Font_pre/下：

# -*- coding: utf-8 -*-
# 获取字体文件名，字体文件可以在百度下载或者电脑字体目录下寻找
import os

def Word_Font():
    Word_Font_Path = 'work/Createfont/Word_Font_pre'
    dirs = os.listdir(Word_Font_Path)
    Word_Font_List = []
    for dir in dirs:
        Word_Font_List.append(dir)
    return Word_Font_List

在work/Createfont/下：

import re

f = open("work/Createfont/9004个常用汉字列表.txt", "r")
lines = f.readlines()  # 读取全部内容 ，并以列表方式返回
Library = []
for line in lines:
    line = line.split('\n')
    line = re.sub(r'\n', "", line[0])
    line = re.sub(':', "", line)
    line = line.replace(" ", "")
    line = line.lstrip('：')
    line = line[:0] + line[13:]
    for i in line:
        Library.append(i)
size_L = len(Library)

# 主程序目录
from PIL import Image, ImageDraw, ImageFont, ImageOps
import os
import re
import Word_Font_pre.word_font as wf

# 读取字体文件
Word_Font_List = wf.Word_Font()


# 选择字体以及图片参数的初始值
class LetterImage():
    def __init__(self, imgSize=(0, 0), imgMode='RGB', bg_color=(255, 255, 255), fg_color=(0, 0, 0),
                 fontsize=10, Word_Font=Word_Font_List[1]):
        self.imgSize = imgSize
        self.imgMode = imgMode
        self.fontsize = fontsize
        self.bg_color = bg_color
        self.fg_color = fg_color
        if(Word_Font.endswith("ttc")):
            self.font = ImageFont.truetype(Word_Font, fontsize, index=1)
        else:
            self.font = ImageFont.truetype(Word_Font, fontsize)

    # 设定生成图片大小
    def GenLetterImage(self, letters):
        self.letters = letters
        (self.letterWidth, self.letterHeight) = self.font.getsize(letters)
        if self.imgSize == (0, 0):
            self.imgSize = (self.letterWidth - 0, self.letterHeight + 15)  # 底边边距
        self.imgWidth, self.imgHeight = self.imgSize
        self.img = Image.new(self.imgMode, self.imgSize, self.bg_color)
        self.drawBrush = ImageDraw.Draw(self.img)
        textY0 = (self.imgHeight - self.letterHeight - 2)
        textY0 = int(textY0)
        textX0 = int((self.imgWidth - self.letterWidth - 2))  # 显示窗口坐标
        self.drawBrush.text((textX0, textY0), self.letters, fill=self.fg_color, font=self.font)


if __name__ == '__main__':
    f = open("work/Createfont/9004个常用汉字列表.txt", "r")
    lines = f.readlines()  # 读取全部内容 ，并以列表方式返回
    Library = []
    for line in lines:
        line = line.split('\n')
        line = re.sub(r'\n', "", line[0])
        line = re.sub(':', "", line)
        line = line.replace(" ", "")
        line = line.lstrip('：')
        # line = line[:0] + line[13:]
        for i in line:
            Library.append(i)
    letterList = []
    # ---------------将图片参数追加到列表以便后期调用--------------------
    for j in range(0, len(Word_Font_List), 1):
        try:
            letterList.append(LetterImage(bg_color=(255, 255, 255), fontsize=100, Word_Font=Word_Font_List[j]))
        finally:
            print(Word_Font_List[j])
        num_letter = len(Library)  # 字体数量
        # ---------------------------创建文件夹------------------------------
        File_name = re.sub(r'\.', '_', Word_Font_List[j])
        paths = os.getcwd()[:-4] + 'Images\\' + File_name  # 获取此py文件路径，在此路径选创建文件夹
        if not os.path.exists(paths):
            os.makedirs(paths)
        paths = paths + "\\"
        # -----------------在某一种字体下，对字库遍历，生成相应字体图片------------------------
        for i in range(num_letter - 1):
            letterList[j].GenLetterImage(Library[i])
            grayImg = ImageOps.grayscale(letterList[j].img)
            grayImg.save(paths + str(i) + ".png")
In [ ] 
# 若你想添加其他的类型字体
# 将字体的ttf或者ttc放入work/Createfont/Word_Font_pre下，直接执行下行代码即可
# !python work/Createfont/main.py

1 数据处理

  1. 制作标签文件label_list.txt
  2. 制作总的训练文件all_list.txt
  3. 打乱并划分为训练集合验证集
In [1] 
!unzip -oq data/data122969/CreatenewImages.zip -d work
In [2] 
# 当然不可避免的第一步，导入库
from sklearn.utils import shuffle
import os
from PIL import Image
import paddle
import random
In [3] 
# -*- coding: utf-8 -*-
# 根据官方paddleclas的提示，我们需要把图像变为两个txt文件
# train_list.txt（训练集）
# val_list.txt（验证集）

# 根据左侧生成的文件夹名字来写根目录
# 训练数据只需要用到训练的，其他两个文件夹不需要读
dirpath = "work/CreatenewImages"
# 先得到总的txt后续再进行划分，因为要划分出验证集，所以要先打乱，因为原本是有序的
def get_all_txt():
    all_list = []
    label_list = []
    i = 0 # 标记总文件数量
    j = 0 # 标记文件类别
    for root,dirs,files in os.walk(dirpath): # 分别代表根目录、文件夹、文件
        # 遍历每个文件去生成all_list集合
        for file in files:
            # 文件中每行格式： 图像相对路径      图像的label_id（数字类别）（注意：中间有空格）。              
            imgpath = os.path.join(root,file)
            # 有坏图，解决掉
            try:
                img = Image.open(imgpath)
            except :
                os.remove(imgpath)
            else :
                img = Image.open(imgpath)
                img.convert('RGB')
                i = i + 1 
                all_list.append(imgpath+" "+str(j-1)+"\n")
        # 遍历每个dir去生成后续需要的label_list集合
        m = 0
        for d in dirs:
            dpath = os.path.join(root,d)
            d = dpath.split("/")[2]
            label_list.append(d+"\n")
            m = m+1
        j = j + 1
    
    # 生成all_list.txt
    allstr = ''.join(all_list)
    f = open('all_list.txt','w',encoding='utf-8')
    f.write(allstr)
    f.close

    # 生成label_list.txt
    labellist = ''.join(label_list)
    f = open('label_list.txt','w',encoding='utf-8')
    f.write(labellist)
    f.close

    return all_list , i
all_list,all_lenth = get_all_txt()
print(all_lenth)
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">216072
</span></span>
In [4] 
# 把数据打乱
all_list = shuffle(all_list)
allstr = ''.join(all_list)
f = open('all_list.txt','w',encoding='utf-8')
f.write(allstr)
f.close()
print("打乱成功，并重新写入文本")
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">打乱成功，并重新写入文本
</span></span>
In [5] 
# 按照比例划分数据集 数据有216072张图片，我这里采用9:1划分
train_size = int(all_lenth * 0.9)
train_list = all_list[:train_size]
val_list = all_list[train_size:]

print(len(train_list))
print(len(val_list))
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">194464
21608
</span></span>
In [6] 
# 运行cell，生成训练集txt 
train_txt = ''.join(train_list)
f_train = open('train_list.txt','w',encoding='utf-8')
f_train.write(train_txt)
f_train.close()
print("train_list.txt 生成成功！")

# 运行cell，生成验证集txt
val_txt = ''.join(val_list)
f_val = open('val_list.txt','w',encoding='utf-8')
f_val.write(val_txt)
f_val.close()
print("val_list.txt 生成成功！")
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">train_list.txt 生成成功！
val_list.txt 生成成功！
</span></span>

此时，如果正确，左侧有如图所示的文件

2 环境搭建

In [1] 
! pip install paddlex
In [2] 
import warnings
warnings.filterwarnings("ignore")

import paddlex as pdx

3 数据预处理

In [64] 
from paddlex import transforms as T

train_transforms = T.Compose([
    T.ResizeByShort(short_size=232, max_size=-1, interp='LINEAR'),
    T.CenterCrop(crop_size=224),
    T.RandomHorizontalFlip(), 
    T.RandomVerticalFlip(),
    T.RandomBlur(prob=0.1),
    T.Normalize()
])

eval_transforms = T.Compose([
    T.ResizeByShort(),
    T.CenterCrop(crop_size=224),
    T.RandomHorizontalFlip(), 
    T.RandomVerticalFlip(),
    T.RandomBlur(prob=0.1),
    T.Normalize()
])

4 定义数据集dataset

In [10] 
train_dataset = pdx.datasets.ImageNet(
    data_dir='',
    file_list='train_list.txt',
    label_list='label_list.txt',
    transforms=train_transforms,
    shuffle=True)
    
eval_dataset = pdx.datasets.ImageNet(
    data_dir='',
    file_list='val_list.txt',
    label_list='label_list.txt',
    transforms=eval_transforms)
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">2021-12-23 17:46:38 [INFO]	Starting to read file list from dataset...
2021-12-23 17:46:40 [INFO]	194464 samples in file train_list.txt
2021-12-23 17:46:40 [INFO]	Starting to read file list from dataset...
2021-12-23 17:46:41 [INFO]	21608 samples in file val_list.txt
</span></span>

5 训练并验证

In [11] 
num_classes = len(train_dataset.labels)
print(num_classes)
model = pdx.cls.ResNet50_vd_ssld(num_classes=num_classes)
model.train(num_epochs=5,
            train_dataset=train_dataset,
            train_batch_size=128,
            eval_dataset=eval_dataset,
            lr_decay_epochs=[6, 8],
            save_interval_epochs=1,
            learning_rate=0.00625,
            save_dir='output/ResNet50_vd_ssld_bs128',
            pretrain_weights=None,
            use_vdl=True)
In [12] 
model.evaluate(eval_dataset, batch_size=64, return_details=False)
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">2021-12-23 18:42:21 [INFO]	Start to evaluate(total_samples=21608, total_steps=338)...
</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">OrderedDict([('acc1', 0.9433432), ('acc5', 0.9998613)])</span></span>

6 简单测试

In [3] 
import paddlex as pdx
model = pdx.load_model('output/ResNet50_vd_ssld_bs128/best_model')
# 这张图片是华文琥珀的杨字
image_name = 'yang.png'
result = model.predict(image_name)
print("Predict Result:", result)
print(result[0]['category'])
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">W1230 19:41:37.423154   101 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W1230 19:41:37.428342   101 device_context.cc:465] device: 0, cuDNN Version: 7.6.
</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">2021-12-30 19:41:40 [INFO]	Model[ResNet50_vd_ssld] loaded.
Predict Result: [{'category_id': 1, 'category': 'STHUPO_TTF', 'score': 0.9962198}]
STHUPO_TTF
</span></span>

7 PaddleOCR的应用

先了解一下基本的PaddleOCR是怎么用的

7.1 安装并测试demo

In [4] 
!pip install "paddleocr>=2.0.1" --no-deps
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting paddleocr>=2.0.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e1/b6/5486e674ce096667dff247b58bf0fb789c2ce17a10e546c2686a2bb07aec/paddleocr-2.3.0.2-py3-none-any.whl (250kB)
     |████████████████████████████████| 256kB 6.3MB/s eta 0:00:01
Installing collected packages: paddleocr
Successfully installed paddleocr-2.3.0.2
</span></span>
In [5] 
!pip install shapely fasttext==0.9.1 imgaug==0.4.0 lmdb lxml premailer pyclipper python-Levenshtein scikit-image
In [6] 
from paddleocr import PaddleOCR, draw_ocr

# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
ocr = PaddleOCR(use_angle_cls=True, lang="ch")  # need to run only once to download and load model into memory
img_path = 'test.png'
result = ocr.ocr(img_path, cls=True)
for line in result:
    print(line)




# 显示结果
from matplotlib import pyplot as plt
from PIL import Image
%matplotlib inline

image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='work/Createfont/Word_Font_pre/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')

plt.figure(figsize=(20,20))
plt.imshow(im_show)
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">[2021/12/30 19:43:00] root WARNING: version PP-OCRv2 not support cls models, auto switch to version PP-OCR
download https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar to /home/aistudio/.paddleocr/2.3.0.2/ocr/det/ch/ch_PP-OCRv2_det_infer/ch_PP-OCRv2_det_infer.tar
</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">  0%|          | 0.00/3.19M [00:00<?, ?iB/s]</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">100%|██████████| 3.19M/3.19M [00:00<00:00, 8.46MiB/s]
</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">download https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar to /home/aistudio/.paddleocr/2.3.0.2/ocr/rec/ch/ch_PP-OCRv2_rec_infer/ch_PP-OCRv2_rec_infer.tar
</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">  0%|          | 0.00/8.88M [00:00<?, ?iB/s]</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">100%|██████████| 8.88M/8.88M [00:01<00:00, 7.90MiB/s]
</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">download https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar to /home/aistudio/.paddleocr/2.3.0.2/ocr/cls/ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.tar
</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">  0%|          | 0.00/1.45M [00:00<?, ?iB/s]</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">100%|██████████| 1.45M/1.45M [00:00<00:00, 4.33MiB/s]
</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">Namespace(benchmark=False, cls_batch_num=6, cls_image_shape='3, 48, 192', cls_model_dir='/home/aistudio/.paddleocr/2.3.0.2/ocr/cls/ch_ppocr_mobile_v2.0_cls_infer', cls_thresh=0.9, cpu_threads=10, det=True, det_algorithm='DB', det_db_box_thresh=0.6, det_db_score_mode='fast', det_db_thresh=0.3, det_db_unclip_ratio=1.5, det_east_cover_thresh=0.1, det_east_nms_thresh=0.2, det_east_score_thresh=0.8, det_limit_side_len=960, det_limit_type='max', det_model_dir='/home/aistudio/.paddleocr/2.3.0.2/ocr/det/ch/ch_PP-OCRv2_det_infer', det_pse_box_thresh=0.85, det_pse_box_type='box', det_pse_min_area=16, det_pse_scale=1, det_pse_thresh=0, det_sast_nms_thresh=0.2, det_sast_polygon=False, det_sast_score_thresh=0.5, drop_score=0.5, e2e_algorithm='PGNet', e2e_char_dict_path='./ppocr/utils/ic15_dict.txt', e2e_limit_side_len=768, e2e_limit_type='max', e2e_model_dir=None, e2e_pgnet_mode='fast', e2e_pgnet_polygon=True, e2e_pgnet_score_thresh=0.5, e2e_pgnet_valid_set='totaltext', enable_mkldnn=False, gpu_mem=500, help='==SUPPRESS==', image_dir=None, ir_optim=True, label_list=['0', '180'], lang='ch', layout_path_model='lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config', max_batch_size=10, max_text_length=25, min_subgraph_size=15, ocr_version='PP-OCRv2', output='./output/table', precision='fp32', process_id=0, rec=True, rec_algorithm='CRNN', rec_batch_num=6, rec_char_dict_path='/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddleocr/ppocr/utils/ppocr_keys_v1.txt', rec_image_shape='3, 32, 320', rec_model_dir='/home/aistudio/.paddleocr/2.3.0.2/ocr/rec/ch/ch_PP-OCRv2_rec_infer', save_log_path='./log_output/', show_log=True, structure_version='STRUCTURE', table_char_dict_path=None, table_char_type='en', table_max_len=488, table_model_dir=None, total_process_num=1, type='ocr', use_angle_cls=True, use_dilation=False, use_gpu=True, use_mp=False, use_onnx=False, use_pdserving=False, use_space_char=True, use_tensorrt=False, vis_font_path='./doc/fonts/simfang.ttf', warmup=True)
[2021/12/30 19:43:04] root DEBUG: dt_boxes num : 2, elapse : 0.023739099502563477
[2021/12/30 19:43:04] root DEBUG: cls num  : 2, elapse : 0.009242057800292969
[2021/12/30 19:43:04] root DEBUG: rec_res num  : 2, elapse : 0.008728265762329102
[[[84.0, 66.0], [463.0, 54.0], [465.0, 125.0], [87.0, 137.0]], ('幸福通道', 0.9984914)]
[[[247.0, 491.0], [395.0, 493.0], [394.0, 530.0], [246.0, 527.0]], ('禁止入内', 0.9945936)]
</span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff"><matplotlib.image.AxesImage at 0x7fbc78407c50></span></span>
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff"><Figure size 1440x1440 with 1 Axes></span></span>

7.2 处理字体

思路：

  1. 把框起来的区域截取作为一张图片
  2. 截取的这张图片进入上面的分类模型进行预测，得到字体的分类结果
  3. 按照PaddleOCR的规则进行输出展示

7.3 导入训练好的模型

In [7] 
import paddlex as pdx
model = pdx.load_model('output/ResNet50_vd_ssld_bs128/best_model')
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">2021-12-30 19:43:15 [INFO]	Model[ResNet50_vd_ssld] loaded.
</span></span>

7.4 ☆ 处理输出

截取片段，然后识别

In [17] 
import os
# 显示结果
from matplotlib import pyplot as plt
from PIL import Image
%matplotlib inline

def jietu_smallpart(imgSrc,result):
    IMG = imgSrc  # 图片地址
    im = Image.open(IMG)  # 用PIL打开一个图片
    im = im.convert("RGB")
    ziti_list=[]
    zitip_list=[]
    i = 0
    for line in result:
        # print(line[0][0][0], line[0][0][1], line[0][2][0], line[0][2][1])
        # box = (line[0][0][0]-2, line[0][0][1]-2, line[0][2][0]+2, line[0][2][1]+2)  
        # box = (line[0][0][0]-2, line[0][0][1]-2, line[0][0][0] + line[0][2][1] - line[0][0][1], line[0][2][1])
        box = (line[0][0][0]-10, line[0][0][1]-10, line[0][2][0]+10, line[0][2][1]+10)
        # box代表需要剪切图片的位置格式为:xmin ymin xmax ymax
        ng = im.crop(box)  # 对im进行裁剪 保存为ng(这里im保持不变)

        # ng = ng.resize((58,58))

        image_name = str(i) + '_copy.jpg'
        i = i+1
        print(image_name)
        ng.save(image_name)
        # 拿到这张图
        result = model.predict(image_name)
        ziti_list.append(result[0]['category'])
        zitip_list.append(result[0]['score'])
    return ziti_list,zitip_list

test,testp = jietu_smallpart("test.png",result)
print(test)
print(testp)
<span style="color:rgba(0, 0, 0, 0.85)"><span style="background-color:#ffffff">0_copy.jpg
1_copy.jpg
['simsun_ttc', 'simsun_ttc']
[0.999925, 0.8224375]
</span></span>

总结

  1. 训练字体的分类，但是目前来看虽然验证集精度很高，在实际的场景中运用，问题还是存在的。
  2. 还没有进行模块化函数式的编程
  3. 后续需要再想办法提升模型的鲁棒性

个人介绍

全网同名：

iterhui

我在AI Studio上获得至尊等级，点亮10个徽章，来互关呀~

https://aistudio.baidu.com/aistudio/personalcenter/thirdview/643467

# 深度学习 # 计算机视觉 # 机器学习
GitHub 加速计划 / pa / PaddleOCR
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7.59 K
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Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
最近提交(Master分支：3 个月前 )
7bbda2bc 10 天前
1d4e7a80 12 天前
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