Initial commit

configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_chinese_common_v2.0
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_common_v2.0.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 5
+  regularizer:
+    name: 'L2'
+    factor: 0.00004
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 34
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 256
+  Head:
+    name: CTCHead
+    fc_decay: 0.00004
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/train_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/val_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_chinese_lite_v2.0
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    warmup_epoch: 5
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/train_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list: ["./train_data/val_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/generate_multi_language_configs.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import yaml
+from argparse import ArgumentParser, RawDescriptionHelpFormatter
+import os.path
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
+support_list = {
+    "it": "italian",
+    "xi": "spanish",
+    "pu": "portuguese",
+    "ru": "russian",
+    "ar": "arabic",
+    "ta": "tamil",
+    "ug": "uyghur",
+    "fa": "persian",
+    "ur": "urdu",
+    "rs": "serbian latin",
+    "oc": "occitan",
+    "rsc": "serbian cyrillic",
+    "bg": "bulgarian",
+    "uk": "ukranian",
+    "be": "belarusian",
+    "te": "telugu",
+    "ka": "kannada",
+    "chinese_cht": "chinese tradition",
+    "hi": "hindi",
+    "mr": "marathi",
+    "ne": "nepali",
+}
+
+latin_lang = [
+    "af",
+    "az",
+    "bs",
+    "cs",
+    "cy",
+    "da",
+    "de",
+    "es",
+    "et",
+    "fr",
+    "ga",
+    "hr",
+    "hu",
+    "id",
+    "is",
+    "it",
+    "ku",
+    "la",
+    "lt",
+    "lv",
+    "mi",
+    "ms",
+    "mt",
+    "nl",
+    "no",
+    "oc",
+    "pi",
+    "pl",
+    "pt",
+    "ro",
+    "rs_latin",
+    "sk",
+    "sl",
+    "sq",
+    "sv",
+    "sw",
+    "tl",
+    "tr",
+    "uz",
+    "vi",
+    "latin",
+]
+arabic_lang = ["ar", "fa", "ug", "ur"]
+cyrillic_lang = [
+    "ru",
+    "rs_cyrillic",
+    "be",
+    "bg",
+    "uk",
+    "mn",
+    "abq",
+    "ady",
+    "kbd",
+    "ava",
+    "dar",
+    "inh",
+    "che",
+    "lbe",
+    "lez",
+    "tab",
+    "cyrillic",
+]
+devanagari_lang = [
+    "hi",
+    "mr",
+    "ne",
+    "bh",
+    "mai",
+    "ang",
+    "bho",
+    "mah",
+    "sck",
+    "new",
+    "gom",
+    "sa",
+    "bgc",
+    "devanagari",
+]
+multi_lang = latin_lang + arabic_lang + cyrillic_lang + devanagari_lang
+
+assert os.path.isfile(
+    "./rec_multi_language_lite_train.yml"
+), "Loss basic configuration file rec_multi_language_lite_train.yml.\
+You can download it from \
+https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/configs/rec/multi_language/"
+
+global_config = yaml.load(
+    open("./rec_multi_language_lite_train.yml", "rb"), Loader=yaml.Loader
+)
+project_path = os.path.abspath(os.path.join(os.getcwd(), "../../../"))
+
+
+class ArgsParser(ArgumentParser):
+    def __init__(self):
+        super(ArgsParser, self).__init__(formatter_class=RawDescriptionHelpFormatter)
+        self.add_argument("-o", "--opt", nargs="+", help="set configuration options")
+        self.add_argument(
+            "-l",
+            "--language",
+            nargs="+",
+            help="set language type, support {}".format(support_list),
+        )
+        self.add_argument(
+            "--train",
+            type=str,
+            help="you can use this command to change the train dataset default path",
+        )
+        self.add_argument(
+            "--val",
+            type=str,
+            help="you can use this command to change the eval dataset default path",
+        )
+        self.add_argument(
+            "--dict",
+            type=str,
+            help="you can use this command to change the dictionary default path",
+        )
+        self.add_argument(
+            "--data_dir",
+            type=str,
+            help="you can use this command to change the dataset default root path",
+        )
+
+    def parse_args(self, argv=None):
+        args = super(ArgsParser, self).parse_args(argv)
+        args.opt = self._parse_opt(args.opt)
+        args.language = self._set_language(args.language)
+        return args
+
+    def _parse_opt(self, opts):
+        config = {}
+        if not opts:
+            return config
+        for s in opts:
+            s = s.strip()
+            k, v = s.split("=")
+            config[k] = yaml.load(v, Loader=yaml.Loader)
+        return config
+
+    def _set_language(self, type):
+        lang = type[0]
+        assert type, "please use -l or --language to choose language type"
+        assert lang in support_list.keys() or lang in multi_lang, (
+            "the sub_keys(-l or --language) can only be one of support list: \n{},\nbut get: {}, "
+            "please check your running command".format(multi_lang, type)
+        )
+        if lang in latin_lang:
+            lang = "latin"
+        elif lang in arabic_lang:
+            lang = "arabic"
+        elif lang in cyrillic_lang:
+            lang = "cyrillic"
+        elif lang in devanagari_lang:
+            lang = "devanagari"
+        global_config["Global"]["character_dict_path"] = (
+            "ppocr/utils/dict/{}_dict.txt".format(lang)
+        )
+        global_config["Global"]["save_model_dir"] = "./output/rec_{}_lite".format(lang)
+        global_config["Train"]["dataset"]["label_file_list"] = [
+            "train_data/{}_train.txt".format(lang)
+        ]
+        global_config["Eval"]["dataset"]["label_file_list"] = [
+            "train_data/{}_val.txt".format(lang)
+        ]
+        global_config["Global"]["character_type"] = lang
+        assert os.path.isfile(
+            os.path.join(project_path, global_config["Global"]["character_dict_path"])
+        ), "Loss default dictionary file {}_dict.txt.You can download it from \
+https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/ppocr/utils/dict/".format(
+            lang
+        )
+        return lang
+
+
+def merge_config(config):
+    """
+    Merge config into global config.
+    Args:
+        config (dict): Config to be merged.
+    Returns: global config
+    """
+    for key, value in config.items():
+        if "." not in key:
+            if isinstance(value, dict) and key in global_config:
+                global_config[key].update(value)
+            else:
+                global_config[key] = value
+        else:
+            sub_keys = key.split(".")
+            assert (
+                sub_keys[0] in global_config
+            ), "the sub_keys can only be one of global_config: {}, but get: {}, please check your running command".format(
+                global_config.keys(), sub_keys[0]
+            )
+            cur = global_config[sub_keys[0]]
+            for idx, sub_key in enumerate(sub_keys[1:]):
+                if idx == len(sub_keys) - 2:
+                    cur[sub_key] = value
+                else:
+                    cur = cur[sub_key]
+
+
+def loss_file(path):
+    assert os.path.exists(
+        path
+    ), "There is no such file:{},Please do not forget to put in the specified file".format(
+        path
+    )
+
+
+if __name__ == "__main__":
+    FLAGS = ArgsParser().parse_args()
+    merge_config(FLAGS.opt)
+    save_file_path = "rec_{}_lite_train.yml".format(FLAGS.language)
+    if os.path.isfile(save_file_path):
+        os.remove(save_file_path)
+
+    if FLAGS.train:
+        global_config["Train"]["dataset"]["label_file_list"] = [FLAGS.train]
+        train_label_path = os.path.join(project_path, FLAGS.train)
+        loss_file(train_label_path)
+    if FLAGS.val:
+        global_config["Eval"]["dataset"]["label_file_list"] = [FLAGS.val]
+        eval_label_path = os.path.join(project_path, FLAGS.val)
+        loss_file(eval_label_path)
+    if FLAGS.dict:
+        global_config["Global"]["character_dict_path"] = FLAGS.dict
+        dict_path = os.path.join(project_path, FLAGS.dict)
+        loss_file(dict_path)
+    if FLAGS.data_dir:
+        global_config["Eval"]["dataset"]["data_dir"] = FLAGS.data_dir
+        global_config["Train"]["dataset"]["data_dir"] = FLAGS.data_dir
+        data_dir = os.path.join(project_path, FLAGS.data_dir)
+        loss_file(data_dir)
+
+    with open(save_file_path, "w") as f:
+        yaml.dump(dict(global_config), f, default_flow_style=False, sort_keys=False)
+    logging.info("Project path is          :{}".format(project_path))
+    logging.info(
+        "Train list path set to   :{}".format(
+            global_config["Train"]["dataset"]["label_file_list"][0]
+        )
+    )
+    logging.info(
+        "Eval list path set to    :{}".format(
+            global_config["Eval"]["dataset"]["label_file_list"][0]
+        )
+    )
+    logging.info(
+        "Dataset root path set to :{}".format(
+            global_config["Eval"]["dataset"]["data_dir"]
+        )
+    )
+    logging.info(
+        "Dict path set to         :{}".format(
+            global_config["Global"]["character_dict_path"]
+        )
+    )
+    logging.info(
+        "Config file set to       :configs/rec/multi_language/{}".format(save_file_path)
+    )

configs/rec/multi_language/rec_arabic_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_arabic_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/arabic_dict.txt
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/arabic_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/arabic_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_cyrillic_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_cyrillic_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/cyrillic_dict.txt
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/cyrillic_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/cyrillic_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_devanagari_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_devanagari_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/devanagari_dict.txt
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/devanagari_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/devanagari_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_en_number_lite_train.yml

+Global:
+  use_gpu: True
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_en_number_lite
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img:
+  # for data or label process
+  character_dict_path: ppocr/utils/en_dict.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/train_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/eval_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_french_lite_train.yml

+Global:
+  use_gpu: True
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_french_lite
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model: 
+  checkpoints: 
+  save_inference_dir: 
+  use_visualdl: False
+  infer_img:
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/french_dict.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/train_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/eval_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_german_lite_train.yml

+Global:
+  use_gpu: True
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_german_lite
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img:
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/german_dict.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/train_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/eval_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_hebrew_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_hebrew_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/hebrew_dict.txt
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/hebrew_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/hebrew_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_japan_lite_train.yml

+Global:
+  use_gpu: True
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_japan_lite
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img:
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/japan_dict.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/train_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/eval_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_korean_lite_train.yml

+Global:
+  use_gpu: True
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_korean_lite
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img:
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/korean_dict.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/train_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: ["./train_data/eval_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_latin_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_latin_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/latin_dict.txt
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/latin_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/latin_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_multi_language_lite_train.yml

+Global:
+  use_gpu: True
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_multi_language_lite
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 2000]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  cal_metric_during_train: True
+  pretrained_model: 
+  checkpoints: 
+  save_inference_dir: 
+  use_visualdl: False
+  infer_img:
+  # for data or label process
+  character_dict_path: 
+  # Set the language of training, if set, select the default dictionary file
+  character_type: 
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list: ["./train_data/train_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list: ["./train_data/val_list.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_samaritan_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_samaritan_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/samaritan_dict.txt
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/samaritan_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/samaritan_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/multi_language/rec_syriac_lite_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_syriac_lite
+  save_epoch_step: 3
+  eval_batch_step:
+  - 0
+  - 2000
+  cal_metric_during_train: true
+  pretrained_model: null
+  checkpoints: null
+  save_inference_dir: null
+  use_visualdl: false
+  infer_img: null
+  character_dict_path: ppocr/utils/dict/syriac_dict.txt
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: L2
+    factor: 1.0e-05
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride:
+    - 1
+    - 2
+    - 2
+    - 2
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 1.0e-05
+Loss:
+  name: CTCLoss
+PostProcess:
+  name: CTCLabelDecode
+Metric:
+  name: RecMetric
+  main_indicator: acc
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/syriac_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug: null
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 256
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/
+    label_file_list:
+    - train_data/syriac_val.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode: null
+    - RecResizeImg:
+        image_shape:
+        - 3
+        - 32
+        - 320
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 256
+    num_workers: 8

configs/rec/rec_d28_can.yml

+Global:
+  use_gpu: True
+  epoch_num: 240
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/can/
+  save_epoch_step: 1
+  # evaluation is run every 1105 iterations (1 epoch)(batch_size = 8)
+  eval_batch_step: [0, 1105]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/datasets/crohme_demo/hme_00.jpg
+  # for data or label process
+  character_dict_path: ppocr/utils/dict/latex_symbol_dict.txt
+  max_text_length: 36
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_can.txt
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  clip_norm_global: 100.0
+  lr:
+    name: TwoStepCosine
+    learning_rate: 0.01
+    warmup_epoch: 1
+  weight_decay: 0.0001
+
+Architecture:
+  model_type: rec
+  algorithm: CAN
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: DenseNet 
+    growthRate: 24
+    reduction: 0.5
+    bottleneck: True
+    use_dropout: True
+    input_channel: 1 
+  Head:
+    name: CANHead
+    in_channel: 684
+    out_channel: 111
+    max_text_length: 36
+    ratio: 16
+    attdecoder:
+      is_train: True
+      input_size: 256
+      hidden_size: 256
+      encoder_out_channel: 684
+      dropout: True
+      dropout_ratio: 0.5
+      word_num: 111
+      counting_decoder_out_channel: 111
+      attention:
+        attention_dim: 512
+        word_conv_kernel: 1
+   
+Loss:
+  name: CANLoss
+
+PostProcess:
+  name: CANLabelDecode
+
+Metric:
+  name: CANMetric
+  main_indicator: exp_rate
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/CROHME/training/images/
+    label_file_list: ["./train_data/CROHME/training/labels.txt"]
+    transforms:
+      - DecodeImage:
+          channel_first: False
+      - NormalizeImage:
+          mean: [0,0,0]
+          std: [1,1,1]
+          order: 'hwc'
+      - GrayImageChannelFormat: 
+          inverse: True
+      - CANLabelEncode:
+          lower: False
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+  loader:
+    shuffle: True
+    batch_size_per_card: 8
+    drop_last: False
+    num_workers: 4
+    collate_fn: DyMaskCollator
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/CROHME/evaluation/images/
+    label_file_list: ["./train_data/CROHME/evaluation/labels.txt"]
+    transforms: 
+      - DecodeImage:
+          channel_first: False
+      - NormalizeImage:
+          mean: [0,0,0]
+          std: [1,1,1]
+          order: 'hwc'
+      - GrayImageChannelFormat:
+          inverse: True
+      - CANLabelEncode:
+          lower: False
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1
+    num_workers: 4
+    collate_fn: DyMaskCollator

configs/rec/rec_efficientb3_fpn_pren.yml

+Global:
+  use_gpu: True
+  epoch_num: 8
+  log_smooth_window: 20
+  print_batch_step: 5
+  save_model_dir: ./output/rec/pren_new
+  save_epoch_step: 3
+  # evaluation is run every 2000 iterations after the 4000th iteration
+  eval_batch_step: [4000, 2000]
+  cal_metric_during_train: True
+  pretrained_model: 
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path:
+  max_text_length: &max_text_length 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_pren.txt
+
+Optimizer:
+  name: Adadelta
+  lr:
+    name: Piecewise
+    decay_epochs: [2, 5, 7]
+    values: [0.5, 0.1, 0.01, 0.001]
+
+Architecture:
+  model_type: rec
+  algorithm: PREN
+  in_channels: 3
+  Backbone:
+    name: EfficientNetb3_PREN
+  Neck:
+    name: PRENFPN
+    n_r: 5
+    d_model: 384
+    max_len: *max_text_length
+    dropout: 0.1
+  Head:
+    name: PRENHead
+
+Loss:
+  name: PRENLoss
+
+PostProcess:
+  name: PRENLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/training/
+    transforms:
+      - DecodeImage:
+          img_mode: BGR
+          channel_first: False
+      - PRENLabelEncode:
+      - RecAug:
+      - PRENResizeImg: 
+          image_shape: [64, 256]  # h,w
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+  loader:
+    shuffle: True
+    batch_size_per_card: 128
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/validation/
+    transforms:
+      - DecodeImage:
+          img_mode: BGR
+          channel_first: False
+      - PRENLabelEncode:
+      - PRENResizeImg: 
+          image_shape: [64, 256]  # h,w
+      - KeepKeys:
+          keep_keys: ['image', 'label']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 64
+    num_workers: 8

configs/rec/rec_icdar15_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/ic15/
+  save_epoch_step: 3
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir: ./
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path: ppocr/utils/en_dict.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_ic15.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 96
+  Head:
+    name: CTCHead
+    fc_decay: 0
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+    use_shared_memory: False
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 4
+    use_shared_memory: False

configs/rec/rec_mtb_nrtr.yml

+Global:
+  use_gpu: True
+  epoch_num: 21
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/nrtr/
+  save_epoch_step: 1
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path: ppocr/utils/EN_symbol_dict.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_nrtr.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.99
+  clip_norm: 5.0
+  lr:
+    name: Cosine
+    learning_rate: 0.0005
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0.
+
+Architecture:
+  model_type: rec
+  algorithm: NRTR
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: MTB
+    cnn_num: 2
+  Head:
+    name: Transformer
+    d_model: 512
+    num_encoder_layers: 6
+    beam_size: -1 # When Beam size is greater than 0, it means to use beam search when evaluation.
+    
+
+Loss:
+  name: CELoss
+  smoothing: True
+
+PostProcess:
+  name: NRTRLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/training/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - NRTRLabelEncode: # Class handling label
+      - GrayRecResizeImg:
+          image_shape: [100, 32] # W H
+          resize_type: PIL # PIL or OpenCV
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 512
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: LMDBDataSet
+    data_dir: ./train_data/data_lmdb_release/evaluation/
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - NRTRLabelEncode: # Class handling label
+      - GrayRecResizeImg:
+          image_shape: [100, 32] # W H
+          resize_type: PIL # PIL or OpenCV
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 4
+    use_shared_memory: False