基于Python的身份证验证识别和数据处理

根据GB11643-1999公民身份证号码是特征组合码，由十七位数字本体码和一位数字校验码组成，排列顺序从左至右依次为：

1. 六位数字地址码
2. 八位数字出生日期码
3. 三位数字顺序码
4. 一位数字校验码（数字10用罗马X表示）

校验系统：

     校验码采用ISO7064:1983，MOD11-2校验码系统（图为校验规则样例）

用身份证号的前17位的每一位号码字符值分别乘上对应的加权因子值，得到的结果求和后对11进行取余，最后的结果放到表2检验码字符值..换算关系表中得出最后的一位身份证号码

代码：

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# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # 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. """Convert BERT checkpoint."""     import argparse   import torch   from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert from transformers.utils import logging     logging.set_verbosity_info()     def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):     # Initialise PyTorch model     config = BertConfig.from_json_file(bert_config_file)     print("Building PyTorch model from configuration: {}".format(str(config)))     model = BertForPreTraining(config)       # Load weights from tf checkpoint     load_tf_weights_in_bert(model, config, tf_checkpoint_path)       # Save pytorch-model     print("Save PyTorch model to {}".format(pytorch_dump_path))     torch.save(model.state_dict(), pytorch_dump_path)     if __name__ == "__main__":     parser = argparse.ArgumentParser()     # Required parameters     parser.add_argument(         "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."     )     parser.add_argument(         "--bert_config_file",         default=None,         type=str,         required=True,         help="The config json file corresponding to the pre-trained BERT model. \n"         "This specifies the model architecture.",     )     parser.add_argument(         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."     )     args = parser.parse_args()     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)