diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md
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+
+## 介绍
+复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型量化将全精度缩减到定点数减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
+模型量化可以在基本不损失模型的精度的情况下，将FP32精度的模型参数转换为Int8精度，减小模型参数大小并加速计算，使用量化后的模型在移动端等部署时更具备速度优势。
+
+本教程将介绍如何使用飞桨模型压缩库PaddleSlim做PaddleOCR模型的压缩。
+[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 集成了模型剪枝、量化（包括量化训练和离线量化）、蒸馏和神经网络搜索等多种业界常用且领先的模型压缩功能，如果您感兴趣，可以关注并了解。
+
+在开始本教程之前，建议先了解[PaddleOCR模型的训练方法](../../../doc/doc_ch/quickstart.md)以及[PaddleSlim](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)
+
+
+## 快速开始
+量化多适用于轻量模型在移动端的部署，当训练出一个模型后，如果希望进一步的压缩模型大小并加速预测，可使用量化的方法压缩模型。
+
+模型量化主要包括五个步骤：
+1. 安装 PaddleSlim
+2. 准备训练好的模型
+3. 量化训练
+4. 导出量化推理模型
+5. 量化模型预测部署
+
+### 1. 安装PaddleSlim
+
+```bash
+git clone https://github.com/PaddlePaddle/PaddleSlim.git
+cd Paddleslim
+python setup.py install
+```
+
+### 2. 准备训练好的模型
+
+PaddleOCR提供了一系列训练好的[模型](../../../doc/doc_ch/models_list.md)，如果待量化的模型不在列表中，需要按照[常规训练](../../../doc/doc_ch/quickstart.md)方法得到训练好的模型。
+
+### 3. 量化训练
+量化训练包括离线量化训练和在线量化训练，在线量化训练效果更好，需加载预训练模型，在定义好量化策略后即可对模型进行量化。
+
+
+量化训练的代码位于slim/quantization/quant.py 中，比如训练检测模型，训练指令如下：
+```bash
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model'   Global.save_model_dir=./output/quant_model
+
+# 比如下载提供的训练模型
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
+tar -xf ch_ppocr_mobile_v2.0_det_train.tar
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_model_dir=./output/quant_model
+
+```
+如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。
+
+### 4. 导出模型
+
+在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
+
+```bash
+python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
+```
+
+### 5. 量化模型部署
+
+上述步骤导出的量化模型，参数精度仍然是FP32，但是参数的数值范围是int8，导出的模型可以通过PaddleLite的opt模型转换工具完成模型转换。
+量化模型部署的可参考 [移动端模型部署](../../lite/readme.md)
diff --git a/deploy/slim/quantization/README_en.md b/deploy/slim/quantization/README_en.md
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+
+## Introduction
+
+Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model.
+Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number,
+so as to reduce model calculation complexity and improve model inference performance.
+
+This example uses PaddleSlim provided [APIs of Quantization](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) to compress the OCR model.
+
+It is recommended that you could understand following pages before reading this example：
+- [The training strategy of OCR model](../../../doc/doc_en/quickstart_en.md)
+- [PaddleSlim Document](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)
+
+## Quick Start
+Quantization is mostly suitable for the deployment of lightweight models on mobile terminals.
+After training, if you want to further compress the model size and accelerate the prediction, you can use quantization methods to compress the model according to the following steps.
+
+1. Install PaddleSlim
+2. Prepare trained model
+3. Quantization-Aware Training
+4. Export inference model
+5. Deploy quantization inference model
+
+
+### 1. Install PaddleSlim
+
+```bash
+git clone https://github.com/PaddlePaddle/PaddleSlim.git
+cd Paddleslim
+python setup.py install
+```
+
+
+### 2. Download Pretrain Model
+PaddleOCR provides a series of trained [models](../../../doc/doc_en/models_list_en.md).
+If the model to be quantified is not in the list, you need to follow the [Regular Training](../../../doc/doc_en/quickstart_en.md) method to get the trained model.
+
+
+### 3. Quant-Aware Training
+Quantization training includes offline quantization training and online quantization training.
+Online quantization training is more effective. It is necessary to load the pre-training model.
+After the quantization strategy is defined, the model can be quantified.
+
+The code for quantization training is located in `slim/quantization/quant.py`. For example, to train a detection model, the training instructions are as follows:
+```bash
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights='your trained model'   Global.save_model_dir=./output/quant_model
+
+# download provided model
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
+tar -xf ch_ppocr_mobile_v2.0_det_train.tar
+python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./ch_ppocr_mobile_v2.0_det_train/best_accuracy   Global.save_model_dir=./output/quant_model
+
+```
+
+
+### 4. Export inference model
+
+After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:
+
+```bash
+python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
+```
+
+### 5. Deploy
+The numerical range of the quantized model parameters derived from the above steps is still FP32, but the numerical range of the parameters is int8.
+The derived model can be converted through the `opt tool` of PaddleLite.
+
+For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme_en.md)
diff --git a/deploy/slim/quantization/export_model.py b/deploy/slim/quantization/export_model.py
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+# Copyright (c) 2020 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 os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
+sys.path.append(
+    os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
+
+import argparse
+
+import paddle
+from paddle.jit import to_static
+
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import init_model
+from ppocr.utils.logging import get_logger
+from tools.program import load_config, merge_config, ArgsParser
+from ppocr.metrics import build_metric
+import tools.program as program
+from paddleslim.dygraph.quant import QAT
+from ppocr.data import build_dataloader
+
+
+def main():
+    ############################################################################################################
+    # 1. quantization configs
+    ############################################################################################################
+    quant_config = {
+        # weight preprocess type, default is None and no preprocessing is performed. 
+        'weight_preprocess_type': None,
+        # activation preprocess type, default is None and no preprocessing is performed.
+        'activation_preprocess_type': None,
+        # weight quantize type, default is 'channel_wise_abs_max'
+        'weight_quantize_type': 'channel_wise_abs_max',
+        # activation quantize type, default is 'moving_average_abs_max'
+        'activation_quantize_type': 'moving_average_abs_max',
+        # weight quantize bit num, default is 8
+        'weight_bits': 8,
+        # activation quantize bit num, default is 8
+        'activation_bits': 8,
+        # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
+        'dtype': 'int8',
+        # window size for 'range_abs_max' quantization. default is 10000
+        'window_size': 10000,
+        # The decay coefficient of moving average, default is 0.9
+        'moving_rate': 0.9,
+        # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
+        'quantizable_layer_type': ['Conv2D', 'Linear'],
+    }
+    FLAGS = ArgsParser().parse_args()
+    config = load_config(FLAGS.config)
+    merge_config(FLAGS.opt)
+    logger = get_logger()
+    # build post process
+
+    post_process_class = build_post_process(config['PostProcess'],
+                                            config['Global'])
+
+    # build model
+    # for rec algorithm
+    if hasattr(post_process_class, 'character'):
+        char_num = len(getattr(post_process_class, 'character'))
+        config['Architecture']["Head"]['out_channels'] = char_num
+    model = build_model(config['Architecture'])
+
+    # get QAT model
+    quanter = QAT(config=quant_config)
+    quanter.quantize(model)
+
+    init_model(config, model, logger)
+    model.eval()
+
+    # build metric
+    eval_class = build_metric(config['Metric'])
+
+    # build dataloader
+    valid_dataloader = build_dataloader(config, 'Eval', device, logger)
+
+    # start eval
+    metirc = program.eval(model, valid_dataloader, post_process_class,
+                          eval_class)
+    logger.info('metric eval ***************')
+    for k, v in metirc.items():
+        logger.info('{}:{}'.format(k, v))
+
+    save_path = '{}/inference'.format(config['Global']['save_inference_dir'])
+    infer_shape = [3, 32, 100] if config['Architecture'][
+        'model_type'] != "det" else [3, 640, 640]
+
+    quanter.save_quantized_model(
+        model,
+        save_path,
+        input_spec=[
+            paddle.static.InputSpec(
+                shape=[None] + infer_shape, dtype='float32')
+        ])
+    logger.info('inference QAT model is saved to {}'.format(save_path))
+
+
+if __name__ == "__main__":
+    config, device, logger, vdl_writer = program.preprocess()
+    main()
diff --git a/deploy/slim/quantization/quant.py b/deploy/slim/quantization/quant.py
new file mode 100755
index 00000000..7671e5f8
--- /dev/null
+++ b/deploy/slim/quantization/quant.py
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+# Copyright (c) 2020 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
+sys.path.append(
+    os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
+
+import yaml
+import paddle
+import paddle.distributed as dist
+
+paddle.seed(2)
+
+from ppocr.data import build_dataloader
+from ppocr.modeling.architectures import build_model
+from ppocr.losses import build_loss
+from ppocr.optimizer import build_optimizer
+from ppocr.postprocess import build_post_process
+from ppocr.metrics import build_metric
+from ppocr.utils.save_load import init_model
+import tools.program as program
+from paddleslim.dygraph.quant import QAT
+
+dist.get_world_size()
+
+
+class PACT(paddle.nn.Layer):
+    def __init__(self):
+        super(PACT, self).__init__()
+        alpha_attr = paddle.ParamAttr(
+            name=self.full_name() + ".pact",
+            initializer=paddle.nn.initializer.Constant(value=20),
+            learning_rate=1.0,
+            regularizer=paddle.regularizer.L2Decay(2e-5))
+
+        self.alpha = self.create_parameter(
+            shape=[1], attr=alpha_attr, dtype='float32')
+
+    def forward(self, x):
+        out_left = paddle.nn.functional.relu(x - self.alpha)
+        out_right = paddle.nn.functional.relu(-self.alpha - x)
+        x = x - out_left + out_right
+        return x
+
+
+quant_config = {
+    # weight preprocess type, default is None and no preprocessing is performed. 
+    'weight_preprocess_type': None,
+    # activation preprocess type, default is None and no preprocessing is performed.
+    'activation_preprocess_type': None,
+    # weight quantize type, default is 'channel_wise_abs_max'
+    'weight_quantize_type': 'channel_wise_abs_max',
+    # activation quantize type, default is 'moving_average_abs_max'
+    'activation_quantize_type': 'moving_average_abs_max',
+    # weight quantize bit num, default is 8
+    'weight_bits': 8,
+    # activation quantize bit num, default is 8
+    'activation_bits': 8,
+    # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
+    'dtype': 'int8',
+    # window size for 'range_abs_max' quantization. default is 10000
+    'window_size': 10000,
+    # The decay coefficient of moving average, default is 0.9
+    'moving_rate': 0.9,
+    # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
+    'quantizable_layer_type': ['Conv2D', 'Linear'],
+}
+
+
+def main(config, device, logger, vdl_writer):
+    # init dist environment
+    if config['Global']['distributed']:
+        dist.init_parallel_env()
+
+    global_config = config['Global']
+
+    # build dataloader
+    train_dataloader = build_dataloader(config, 'Train', device, logger)
+    if config['Eval']:
+        valid_dataloader = build_dataloader(config, 'Eval', device, logger)
+    else:
+        valid_dataloader = None
+
+    # build post process
+    post_process_class = build_post_process(config['PostProcess'],
+                                            global_config)
+
+    # build model
+    # for rec algorithm
+    if hasattr(post_process_class, 'character'):
+        char_num = len(getattr(post_process_class, 'character'))
+        config['Architecture']["Head"]['out_channels'] = char_num
+    model = build_model(config['Architecture'])
+
+    # prepare to quant
+    quanter = QAT(config=quant_config, act_preprocess=PACT)
+    quanter.quantize(model)
+
+    if config['Global']['distributed']:
+        model = paddle.DataParallel(model)
+
+    # build loss
+    loss_class = build_loss(config['Loss'])
+
+    # build optim
+    optimizer, lr_scheduler = build_optimizer(
+        config['Optimizer'],
+        epochs=config['Global']['epoch_num'],
+        step_each_epoch=len(train_dataloader),
+        parameters=model.parameters())
+
+    # build metric
+    eval_class = build_metric(config['Metric'])
+    # load pretrain model
+    pre_best_model_dict = init_model(config, model, logger, optimizer)
+
+    logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
+                format(len(train_dataloader), len(valid_dataloader)))
+    # start train
+    program.train(config, train_dataloader, valid_dataloader, device, model,
+                  loss_class, optimizer, lr_scheduler, post_process_class,
+                  eval_class, pre_best_model_dict, logger, vdl_writer)
+
+
+def test_reader(config, device, logger):
+    loader = build_dataloader(config, 'Train', device, logger)
+    import time
+    starttime = time.time()
+    count = 0
+    try:
+        for data in loader():
+            count += 1
+            if count % 1 == 0:
+                batch_time = time.time() - starttime
+                starttime = time.time()
+                logger.info("reader: {}, {}, {}".format(
+                    count, len(data[0]), batch_time))
+    except Exception as e:
+        logger.info(e)
+    logger.info("finish reader: {}, Success!".format(count))
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess(is_train=True)
+    main(config, device, logger, vdl_writer)
+    # test_reader(config, device, logger)