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Rewrite Version of clovaai/deep-text-recognition-benchmark. Made attempts of deployment on edge devices.

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unanan/deep-text-recognition-benchmark-mnn-ncnn

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deep-text-recognition-benchmark-mnn-ncnn

Brief

Training

1. Prepare datasets

2. Start to train

  • Modify the values in train.py:
Parameters Position Remarks
train_data train.py, Line:223 lmdb trainset folder
valid_data train.py, Line:224 lmdb valset folder
batch_max_length train.py, Line:246 width of the resized image
imgH train.py, Line:247 height of the resized image
imgW train.py, Line:248 width of the resized image
character train.py, Line:250 all your characters of your vocabulary you want to recognize
  • IMPORTANT:If there're special characters in your vocabulary(e.g."-"), please modify the dataset.py Line:170.

  • The output weights (end with .pth) are saved in the ./saved_models by default.

  • Then run python train.py.

Visualization

  • You can use netron to open your *.pth to view the model architecture.

Inference

Ideas & Issues

  • Be free to open issues or pull requests.

TODOs

Not Finished yet!!!

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Rewrite Version of clovaai/deep-text-recognition-benchmark. Made attempts of deployment on edge devices.

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