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NRTR

1. Introduction

Paper:

NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition Fenfen Sheng and Zhineng Chen and Bo Xu ICDAR, 2019

Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:

Model Backbone config Acc Download link
NRTR MTB rec_mtb_nrtr.yml 84.21% trained model

2. Environment

Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone" to clone the project code.

3. Model Training / Evaluation / Prediction

Please refer to Text Recognition Tutorial. PaddleOCR modularizes the code, and training different recognition models only requires changing the configuration file.

Training:

Specifically, after the data preparation is completed, the training can be started. The training command is as follows:

#Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_mtb_nrtr.yml

#Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_mtb_nrtr.yml

Evaluation:

# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_mtb_nrtr.yml -o Global.pretrained_model={path/to/weights}/best_accuracy

Prediction:

# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_mtb_nrtr.yml -o Global.infer_img='./doc/imgs_words_en/word_10.png' Global.pretrained_model=./rec_mtb_nrtr_train/best_accuracy

4. Inference and Deployment

4.1 Python Inference

First, the model saved during the NRTR text recognition training process is converted into an inference model. ( Model download link) ), you can use the following command to convert:

python3 tools/export_model.py -c configs/rec/rec_mtb_nrtr.yml -o Global.pretrained_model=./rec_mtb_nrtr_train/best_accuracy  Global.save_inference_dir=./inference/rec_mtb_nrtr

Note:

  • If you are training the model on your own dataset and have modified the dictionary file, please pay attention to modify the character_dict_path in the configuration file to the modified dictionary file.
  • If you modified the input size during training, please modify the infer_shape corresponding to NRTR in the tools/export_model.py file.

After the conversion is successful, there are three files in the directory:

/inference/rec_mtb_nrtr/
    ├── inference.pdiparams
    ├── inference.pdiparams.info
    └── inference.pdmodel

For NRTR text recognition model inference, the following commands can be executed:

python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words_en/word_10.png' --rec_model_dir='./inference/rec_mtb_nrtr/' --rec_algorithm='NRTR' --rec_image_shape='1,32,100' --rec_char_dict_path='./ppocr/utils/EN_symbol_dict.txt'

After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows: The result is as follows:

Predicts of ./doc/imgs_words_en/word_10.png:('pain', 0.9465042352676392)

4.2 C++ Inference

Not supported

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

  1. In the NRTR paper, Beam search is used to decode characters, but the speed is slow. Beam search is not used by default here, and greedy search is used to decode characters.

6. Release Note

  1. The release/2.6 version updates the NRTR code structure. The new version of NRTR can load the model parameters of the old version (release/2.5 and before), and you may use the following code to convert the old version model parameters to the new version model parameters:
    params = paddle.load('path/' + '.pdparams') # the old version parameters
    state_dict = model.state_dict() # the new version model parameters
    new_state_dict = {}

    for k1, v1 in state_dict.items():

        k = k1
        if 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:

            k_para = k[:13] + 'layers.' + k[13:]
            q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
            k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
            v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))

            new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)

        elif 'encoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:

            k_para = k[:13] + 'layers.' + k[13:]
            q = params[k_para.replace('qkv', 'conv1')]
            k = params[k_para.replace('qkv', 'conv2')]
            v = params[k_para.replace('qkv', 'conv3')]

            new_state_dict[k1] = np.concatenate([q, k, v], -1)

        elif 'encoder' in k and 'self_attn' in k and 'out_proj' in k:

            k_para = k[:13] + 'layers.' + k[13:]
            new_state_dict[k1] = params[k_para]

        elif 'encoder' in k and 'norm3' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            new_state_dict[k1] = params[k_para.replace('norm3', 'norm2')]

        elif 'encoder' in k and 'norm1' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            new_state_dict[k1] = params[k_para]


        elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'weight' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            q = params[k_para.replace('qkv', 'conv1')].transpose((1, 0, 2, 3))
            k = params[k_para.replace('qkv', 'conv2')].transpose((1, 0, 2, 3))
            v = params[k_para.replace('qkv', 'conv3')].transpose((1, 0, 2, 3))
            new_state_dict[k1] = np.concatenate([q[:, :, 0, 0], k[:, :, 0, 0], v[:, :, 0, 0]], -1)

        elif 'decoder' in k and 'self_attn' in k and 'qkv' in k and 'bias' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            q = params[k_para.replace('qkv', 'conv1')]
            k = params[k_para.replace('qkv', 'conv2')]
            v = params[k_para.replace('qkv', 'conv3')]
            new_state_dict[k1] = np.concatenate([q, k, v], -1)

        elif 'decoder' in k and 'self_attn' in k and 'out_proj' in k:

            k_para = k[:13] + 'layers.' + k[13:]
            new_state_dict[k1] = params[k_para]

        elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'weight' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            k_para = k_para.replace('cross_attn', 'multihead_attn')
            q = params[k_para.replace('q', 'conv1')].transpose((1, 0, 2, 3))
            new_state_dict[k1] = q[:, :, 0, 0]

        elif 'decoder' in k and 'cross_attn' in k and 'q' in k and 'bias' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            k_para = k_para.replace('cross_attn', 'multihead_attn')
            q = params[k_para.replace('q', 'conv1')]
            new_state_dict[k1] = q

        elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'weight' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            k_para = k_para.replace('cross_attn', 'multihead_attn')
            k = params[k_para.replace('kv', 'conv2')].transpose((1, 0, 2, 3))
            v = params[k_para.replace('kv', 'conv3')].transpose((1, 0, 2, 3))
            new_state_dict[k1] = np.concatenate([k[:, :, 0, 0], v[:, :, 0, 0]], -1)

        elif 'decoder' in k and 'cross_attn' in k and 'kv' in k and 'bias' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            k_para = k_para.replace('cross_attn', 'multihead_attn')
            k = params[k_para.replace('kv', 'conv2')]
            v = params[k_para.replace('kv', 'conv3')]
            new_state_dict[k1] = np.concatenate([k, v], -1)

        elif 'decoder' in k and 'cross_attn' in k and 'out_proj' in k:

            k_para = k[:13] + 'layers.' + k[13:]
            k_para = k_para.replace('cross_attn', 'multihead_attn')
            new_state_dict[k1] = params[k_para]
        elif 'decoder' in k and 'norm' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            new_state_dict[k1] = params[k_para]
        elif 'mlp' in k and 'weight' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            k_para = k_para.replace('fc', 'conv')
            k_para = k_para.replace('mlp.', '')
            w = params[k_para].transpose((1, 0, 2, 3))
            new_state_dict[k1] = w[:, :, 0, 0]
        elif 'mlp' in k and 'bias' in k:
            k_para = k[:13] + 'layers.' + k[13:]
            k_para = k_para.replace('fc', 'conv')
            k_para = k_para.replace('mlp.', '')
            w = params[k_para]
            new_state_dict[k1] = w

        else:
            new_state_dict[k1] = params[k1]

        if list(new_state_dict[k1].shape) != list(v1.shape):
            print(k1)


    for k, v1 in state_dict.items():
        if k not in new_state_dict.keys():
            print(1, k)
        elif list(new_state_dict[k].shape) != list(v1.shape):
            print(2, k)



    model.set_state_dict(new_state_dict)
    paddle.save(model.state_dict(), 'nrtrnew_from_old_params.pdparams')
  1. The new version has a clean code structure and improved inference speed compared with the old version.

Citation

@article{Sheng2019NRTR,
  title     = {NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition},
  author    = {Fenfen Sheng and Zhineng Chen and Bo Xu},
  booktitle = {ICDAR},
  year      = {2019},
  url       = {http://arxiv.org/abs/1806.00926},
  pages     = {781-786}
}