Train a Custom OCR Model with CDistNet (with Code)

CDistNet Model Architecture

Similar to what I mentioned about PARSeq in my previous blog post, CDistNet, too, is a model that’s been performing very well at Scene Text Recognition (STR). In fact, this model performed better for my use case (Sinhala OCR) than PARSeq. So, it might be a good idea to give this a try if PARSeq didn’t work too well for your use case.

• Steps to prepare for training CDistNet
  • 1. Clone the CDistNet repository
  • 2. Create the dataset
  • 3. Change the character set (optional)
  • 4. Change the config file (CDistNet_config.py)
    • Optional Changes
  • 5. Few changes to requirements.txt
  • 6. (For Windows users only) Change the tensorboard logging format
• Train CDistNet
• Run in Colab
• Final Thoughts

Steps to prepare for training CDistNet

All the changes that I’ve made to get the original repo to work for my use case can be found here. However, you may not need all the changes. So, I’ll discuss each of them below.

1. Clone the CDistNet repository

You can find the official repository of CDistNet here on GitHub and you can clone it using the following command:

git clone https://github.com/simplify23/CDistNet.git

2. Create the dataset

If you, by any chance, already created an LMDB dataset, either during the previous blog post, or otherwise, then you can skip this section. If not, then:

• Download the create_lmdb_dataset.py file from here

• Follow Step 3 of the previous blog post. Just the “Prerequisites” and “Create the dataset” sub-steps are enough.

3. Change the character set (optional)

Do this step if the characters you’re training for are different to the default (0-9 + a-z).

• Inside the cloned repo, visit the path cdistnet/utils/.

• Duplicate the dict_36.txt file and rename it to include the number of characters in your alphabet (instead of 36).

• Replace the characters in their alphabet with those of yours. Leave the first 4 characters as is.

4. Change the config file (CDistNet_config.py)

• Inside the clone repo open up configs/CDistNet_config.py.

• Change the dst_vocab variable to be the path to the file created in Step 3

• Change the dst_vocab_size variable to be the number of lines in the file created in Step 3 (total number of characters).

• Replace the paths for gt_file in the train dictionary with the path(s) to your training dataset(s) (the folder created in Step 2).

Changes for gt_file in CDistNet_config.py

• Do the same for the val and test dictionaries.

Optional Changes

• Set num_workers=0 in the train, val and test dictionaries if its causing errors.

• Reduce batch_size if memory is insufficient.

• I’ve assigned the dataset paths to environment variables so that I can easily change them via a notebook file.

5. Few changes to requirements.txt

My local tests were executed with Python 3.8.9. However, this version of Python did not include torch==1.5.0, so I used a later version (torch==1.8.0). Also, to install the CUDA version, I used the following command:

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html

You may also have to install lmdb if not already installed. Finally, I had an issue with the automatically installed protobuf version so I added a later version for that as well (protobuf==3.20.2).

Changes to requirements.txt in CDistNet

6. (For Windows users only) Change the tensorboard logging format

Windows doesn’t allow the usage of colons (:) in their filenames, so make the following change:

CDistNet tensorboard logging change for Windows

Train CDistNet

If you managed to setup everything correctly, simply running the following command should start the training process:

python train.py --config configs\CDistNet_config.py

During training, the best model at each epoch would be saved to models/reconstruct_CDistNet_3_10/. So, you can change this path from the config file in Step 4, as well.

Run in Colab

To train the model on colab, feel free to use the following notebook. To do so, simply change the path names to the name of your fork of the original repo and you should be good to go.

Final Thoughts

All done! Hope you’ll find this post useful. Also, feel free to ask about any issues you may run into. Finally, if CDistNet didn’t work too well for you, then check out this blog post, which includes links to train a custom model using several other SOTA models.

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