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README
Apache-2.0

text-detection

简介

为了能将一张图像中的多行文本识别出来,可以将该任务分为两步:

  1. 检测图像中每一行文本的位置
  2. 根据位置从原始图像截取出一堆子图像
  3. 只需识别出子图像的文字,再进行排序组合即可

因此,采用两类模型:

  1. 文本检测:CTPN
  2. 文本识别:Densenet + ctc

安装

运行环境

OS: win10 Python: 3.6

安装步骤

  1. 安装tensorflow 1.9.0,若电脑配置了gpu环境,请选择gpu版,否则选择cpu版
pip install tensorflow==1.9.0 # 1. for cpu
pip install tensorflow-gpu==1.9.0 # 2.  for gpu
  1. 安装该包
python setup.py sdist
cd dist/
pip install dlocr-0.1.tar.gz

执行速度

图像大小 处理器 文本行数量 速度
500kb 1070ti 20 420ms
500kb Tesla k80 20 1s

使用

OCR

用于识别一张图片中的文字

  • 编程方式

    import time
    import dlocr
    
    if __name__ == '__main__':
        ocr = dlocr.get_or_create()
        start = time.time()
    
        bboxes, texts = ocr.detect("../asset/demo_ctpn.png")
        print('\n'.join(texts))
        print(f"cost: {(time.time() - start) * 1000}ms")

    get_or_create() 支持以下参数用于使用自己训练的模型:

    • ctpn_weight_path
    • ctpn_config_path
    • densenet_weight_path
    • densenet_config_path
    • dict_path

    参数说明:见以下命令行方式中的参数说明。

  • 命令行方式

    > python -m dlocr -h
    
    usage: text_detection_app.py [-h] [--image_path IMAGE_PATH]
                                [--dict_file_path DICT_FILE_PATH]
                                [--densenet_config_path DENSENET_CONFIG_PATH]
                                [--ctpn_config_path CTPN_CONFIG_PATH]
                                [--ctpn_weight_path CTPN_WEIGHT_PATH]
                                [--densenet_weight_path DENSENET_WEIGHT_PATH]
                                [--adjust ADJUST]
    
    optional arguments:
      -h, --help            show this help message and exit
      --image_path IMAGE_PATH
                            图像位置
      --dict_file_path DICT_FILE_PATH
                            字典文件位置
      --densenet_config_path DENSENET_CONFIG_PATH
                            densenet模型配置文件位置
      --ctpn_config_path CTPN_CONFIG_PATH
                            ctpn模型配置文件位置
      --ctpn_weight_path CTPN_WEIGHT_PATH
                            ctpn模型权重文件位置
      --densenet_weight_path DENSENET_WEIGHT_PATH
                            densenet模型权重文件位置
      --adjust ADJUST       是否对倾斜的文本进行旋转
    
  1. ctpn模型权重文件位置不指定默认使用weights/weights-ctpnlstm-init.hdf5
  2. ctpn模型配置文件位置不指定默认使用config/ctpn-default.json
  3. densenet模型权重文件位置不指定默认使用weights/weights-densent-init.hdf5
  4. densenet模型配置文件位置不指定默认使用config/densent-default.json
  5. 字典文件位置不指定默认使用dictionary/char_std_5990.txt

示例:

python -m dlocr  --image_path asset/demo_ctpn.png

CTPN

用于定于图像中文字的位置

  • 编程方式

    from dlocr import ctpn
    
    if __name__ == '__main__':
        ctpn = ctpn.get_or_create()
        ctpn.predict("asset/demo_ctpn.png", "asset/demo_ctpn_labeled.jpg")
  • 命令行方式

    > python dlocr.ctpn_predict.py -h
    
    usage: ctpn_predict.py [-h] [--image_path IMAGE_PATH]
                          [--config_file_path CONFIG_FILE_PATH]
                          [--weights_file_path WEIGHTS_FILE_PATH]
                          [--output_file_path OUTPUT_FILE_PATH]
    
    optional arguments:
      -h, --help            show this help message and exit
      --image_path IMAGE_PATH
                            图像位置
      --config_file_path CONFIG_FILE_PATH
                            模型配置文件位置
      --weights_file_path WEIGHTS_FILE_PATH
                            模型权重文件位置
      --output_file_path OUTPUT_FILE_PATH
                            标记文件保存位置
    1. 权重文件位置不指定默认使用weights/weights-ctpnlstm-init.hdf5
    2. 配置文件位置不指定默认使用config/ctpn-default.json

    示例:

    python ctpn_predict.py --image_path asset/demo_ctpn.png --output_file_path asset/demo_ctpn_labeled.jpg

Densenet

用于识别固定图像高度中的文字,默认图像高度为32

  • 编程方式

    from dlocr.densenet import load_dict, default_dict_path
    from dlocr import densenet
    
    if __name__ == '__main__':
        densenet = densenet.get_or_create()
        text, img = densenet.predict("asset/demo_densenet.jpg", load_dict(default_dict_path))
        print(text)
  • 命令行方式

    > python dlocr.densenet_predict.py -h
    
    usage: densenetocr_predict.py [-h] [--image_path IMAGE_PATH]
                                  [--dict_file_path DICT_FILE_PATH]
                                  [--config_file_path CONFIG_FILE_PATH]
                                  [--weights_file_path WEIGHTS_FILE_PATH]
    
    optional arguments:
      -h, --help            show this help message and exit
      --image_path IMAGE_PATH
                            图像位置
      --dict_file_path DICT_FILE_PATH
                            字典文件位置
      --config_file_path CONFIG_FILE_PATH
                            模型配置文件位置
      --weights_file_path WEIGHTS_FILE_PATH
                            模型权重文件位置
  1. 权重文件位置不指定默认使用weights/weights-densent-init.hdf5
  2. 配置文件位置不指定默认使用config/densent-default.json
  3. 字典文件位置不指定默认使用dictionary/char_std_5990.txt

示例:

python densenetocr_predict.py --image_path asset/demo_densenet.jpg

训练

数据集说明

  • CTPN 训练使用的数据集格式与VOC数据集格式相同,目录格式如下:

    - VOCdevkit
        - VOC2007
            - Annotations
            - ImageSets
            - JPEGImages
  • Densenet + ctc 使用的数据集分为3部分

    • 文字图像
    • 标注文件:包括图像路径与所对应的文本标记(train.txt, test.txt)
    • 字典文件:包含数据集中的所有文字 (char_std_5990.txt)

数据集链接:

关于创建自己的文本识别数据集,可参考:https://github.com/Sanster/text_renderer

CTPN 训练

> python -m dlocr.ctpn_train -h

usage: ctpn_train.py [-h] [-ie INITIAL_EPOCH] [--epochs EPOCHS] [--gpus GPUS]
                     [--images_dir IMAGES_DIR] [--anno_dir ANNO_DIR]
                     [--config_file_path CONFIG_FILE_PATH]
                     [--weights_file_path WEIGHTS_FILE_PATH]
                     [--save_weights_file_path SAVE_WEIGHTS_FILE_PATH]

optional arguments:
  -h, --help            show this help message and exit
  -ie INITIAL_EPOCH, --initial_epoch INITIAL_EPOCH
                        初始迭代数
  --epochs EPOCHS       迭代数
  --gpus GPUS           gpu的数量
  --images_dir IMAGES_DIR
                        图像位置
  --anno_dir ANNO_DIR   标注文件位置
  --config_file_path CONFIG_FILE_PATH
                        模型配置文件位置
  --weights_file_path WEIGHTS_FILE_PATH
                        模型初始权重文件位置
  --save_weights_file_path SAVE_WEIGHTS_FILE_PATH
                        保存模型训练权重文件位置

ctpn 的训练需要传入2个必要参数:

  1. 图像目录位置
  2. 标注文件目录位置

<模型配置文件位置> 用于指定模型的一些参数,若不指定,将使用默认配置:

{
  "image_channels": 3,  // 图像通道数
  "vgg_trainable": true, // vgg 模型是否可训练
  "lr": 1e-05   // 初始学习率
}

<保存模型训练权重文件位置> 若不指定,会保存到当前目录下的model文件夹

训练情况:

...

Epoch 17/20
6000/6000 [==============================] - 4036s 673ms/step - loss: 0.0895 - rpn_class_loss: 0.0360 - rpn_regress_loss: 0.0534
Epoch 18/20
6000/6000 [==============================] - 4075s 679ms/step - loss: 0.0857 - rpn_class_loss: 0.0341 - rpn_regress_loss: 0.0516
Epoch 19/20
6000/6000 [==============================] - 4035s 673ms/step - loss: 0.0822 - rpn_class_loss: 0.0324 - rpn_regress_loss: 0.0498
Epoch 20/20
6000/6000 [==============================] - 4165s 694ms/step - loss: 0.0792 - rpn_class_loss: 0.0308 - rpn_regress_loss: 0.0484

Densenet 训练

> python -m dlocr.densenet_train -h
usage: densenet_train.py [-h] [-ie INITIAL_EPOCH] [-bs BATCH_SIZE]
                         [--epochs EPOCHS] [--gpus GPUS]
                         [--images_dir IMAGES_DIR]
                         [--dict_file_path DICT_FILE_PATH]
                         [--train_file_path TRAIN_FILE_PATH]
                         [--test_file_path TEST_FILE_PATH]
                         [--config_file_path CONFIG_FILE_PATH]
                         [--weights_file_path WEIGHTS_FILE_PATH]
                         [--save_weights_file_path SAVE_WEIGHTS_FILE_PATH]

optional arguments:
  -h, --help            show this help message and exit
  -ie INITIAL_EPOCH, --initial_epoch INITIAL_EPOCH
                        初始迭代数
  -bs BATCH_SIZE, --batch_size BATCH_SIZE
                        小批量处理大小
  --epochs EPOCHS       迭代数
  --gpus GPUS           gpu的数量
  --images_dir IMAGES_DIR
                        图像位置
  --dict_file_path DICT_FILE_PATH
                        字典文件位置
  --train_file_path TRAIN_FILE_PATH
                        训练文件位置
  --test_file_path TEST_FILE_PATH
                        测试文件位置
  --config_file_path CONFIG_FILE_PATH
                        模型配置文件位置
  --weights_file_path WEIGHTS_FILE_PATH
                        模型初始权重文件位置
  --save_weights_file_path SAVE_WEIGHTS_FILE_PATH
                        保存模型训练权重文件位置

Densnet 的训练需要4个必要参数:

  1. 训练图像位置
  2. 字典文件位置
  3. 训练文件位置
  4. 测试文件位置

<模型配置文件位置> 用于指定模型使用的配置文件路径,若不指定,默认配置如下:

{
  "lr": 0.0005, // 初始学习率
  "num_classes": 5990, // 字典大小
  "image_height": 32,   // 图像高
  "image_channels": 1,  // 图像通道数
  "maxlen": 50,         // 最长文本长度
  "dropout_rate": 0.2,  //  随机失活率
  "weight_decay": 0.0001, // 权重衰减率
  "filters": 64         // 模型第一层的核数量
}

<保存模型训练权重文件位置> 若不指定,会保存到当前目录下的model文件夹

训练情况:

Epoch 3/100
25621/25621 [==============================] - 15856s 619ms/step - loss: 0.1035 - acc: 0.9816 - val_loss: 0.1060 - val_acc: 0.9823
Epoch 4/100
25621/25621 [==============================] - 15651s 611ms/step - loss: 0.0798 - acc: 0.9879 - val_loss: 0.0848 - val_acc: 0.9878
Epoch 5/100
25621/25621 [==============================] - 16510s 644ms/step - loss: 0.0732 - acc: 0.9889 - val_loss: 0.0815 - val_acc: 0.9881
Epoch 6/100
25621/25621 [==============================] - 15621s 610ms/step - loss: 0.0691 - acc: 0.9895 - val_loss: 0.0791 - val_acc: 0.9886
Epoch 7/100
25621/25621 [==============================] - 15782s 616ms/step - loss: 0.0666 - acc: 0.9899 - val_loss: 0.0787 - val_acc: 0.9887
Epoch 8/100
25621/25621 [==============================] - 15560s 607ms/step - loss: 0.0645 - acc: 0.9903 - val_loss: 0.0771 - val_acc: 0.9888

其它

训练好的权重文件

链接: https://pan.baidu.com/s/1HaeLO-fV_WCtTZl4DQvrzw 提取码: ihdx

参考

  1. https://github.com/YCG09/chinese_ocr
  2. https://github.com/xiaomaxiao/keras_ocr
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简介

Chinese text detection and recognition based on CTPN + DENSENET using Keras and Tensor Flow,使用keras和tensorflow基于CTPN+Densenet实现的中文文本检测和识别 展开 收起
Python 等 2 种语言
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