PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition

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Deep Learning CdistNet-pytorch
Overview

PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition

The unofficial code of CDistNet.

Now, we have implemented all the modules according to the papaer except for TPS in the visual branch.You can refer ASTER for the implementation of TPS.

Requirements

Python3.6.8
lmdb==0.98
torch==1.5.1
torchvision==0.6.1
Pillow==6.1.0
opencv-python==4.2.0.32
numpy==1.17.1

Data preparation

We offer you a tool to transform raw dataset to LMDB dataset. Details please refer to tools/create_lmdb_dataset.py

You can also download lmdb dataset from OCR_Dataset

Train

First you need to modify some arguments in configs/cdistnet.yml.

  • TrainReader set the path of train lmdb dataset.
  • EvalReader set the path of evaluation lmdb dataset.
  • Global set the args like image_shape, dict_file, etc.
  • VisualModule set the args of visual branch in the original paper.
  • PositionalEmbedding set the args of positional branch.
  • SemanticEmbedding set the args of semantic branch.
  • MDCDP set the args of MDCDP.
python train.py -c configs/cdistnet.yml

Demo

Modify these arguments below in configs/cdistnet.yml.

  • pretrain_weights set the path of model file path.
  • infer_img set the image path.
  • `is_train set to False.
python predict.py -c configs/cdistnet.yml

TODO

  • Pretrained models
  • Test code
  • Comparison with original paper on benchmarks(CUTE, IC13, IC15, IIIT5K, SVT, SVTP)
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Comments
  • Possible to train only language modelling?

    Possible to train only language modelling?

    I have a relatively small dataset of a different format (license plates) and it often gets license plate format wrong.

    I was wondering if there was a way to train the model on just a bunch of text string data without feeding any images at all in order to enforce the format.

    Please let me know if it is possible to train the language/semantic model independently, by just feeding string text data of words, without corresponding images.

    opened by siddagra 0
  • Faced PicklingError

    Faced PicklingError 

    2022-02-02 16:31:18,549 [INFO ]  dataset_root:    root\is\here
         dataset: \
sub-directory:  /.       num samples: 3736
num total samples of total dataset is 3736


2022-02-02 16:31:18,559 [INFO ]  num total samples of \: 3736 x 1.0 (total_data_usage_ratio) = 3736
num samples of \ per batch: 32 x 1.0 (batch_ratio) = 32

Traceback (most recent call last):
  File "train.py", line 71, in <module>
    main()
  File "train.py", line 48, in main
    train_loader = build_data_loader(flags, mode='train')
  File "root\is\here\CdistNet2\program.py", line 84, in build_data_loader
    dataloader = create_module(dataloader_infor)(flags)
  File "root\is\here\CdistNet2\dataset.py", line 114, in __init__
    self.dataloader_iter_list.append(iter(_data_loader))
  File "I:\Anaconda\envs\CDistNet2\lib\site-packages\torch\utils\data\dataloader.py", line 279, in __iter__
    return _MultiProcessingDataLoaderIter(self)
  File "I:\Anaconda\envs\CDistNet2\lib\site-packages\torch\utils\data\dataloader.py", line 719, in __init__
    w.start()
  File "I:\Anaconda\envs\CDistNet2\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)
  File "I:\Anaconda\envs\CDistNet2\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "I:\Anaconda\envs\CDistNet2\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "I:\Anaconda\envs\CDistNet2\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
    reduction.dump(process_obj, to_child)
  File "I:\Anaconda\envs\CDistNet2\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class 'flags.FLAGS'>: attribute lookup FLAGS on flags failed

(CDistNet2) I:\Google_Drive\Hyundai\Learnin\CdistNet2>Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "I:\Anaconda\envs\CDistNet2\lib\multiprocessing\spawn.py", line 105, in spawn_main
    exitcode = _main(fd)
  File "I:\Anaconda\envs\CDistNet2\lib\multiprocessing\spawn.py", line 115, in _main
    self = reduction.pickle.load(from_parent)
EOFError: Ran out of input

    I don't know what is problem exactly but Looks like problem of namedtuple I guess??
    Is there any way to get around this problem?

    opened by qkfxhqrkrrl 0
  • Not predicting any text

    Not predicting any text

    Thank you for your work. Training accuracy is good but when I tried to predict not predicting any text all are empty. could you please let me know what is the problem?

    opened by bharatsubedi 0
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