Update README.md
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README.md
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@@ -4,3 +4,50 @@ Fine-tuned standard easyocr model using the following dataset: https://huggingfa
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train_log: https://huggingface.co/fimu-docproc-research/standard_0.1.1_EasyOcrEngine/blob/main/log_train.txt
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options: https://huggingface.co/fimu-docproc-research/standard_0.1.1_EasyOcrEngine/blob/main/opt.txt
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train_log: https://huggingface.co/fimu-docproc-research/standard_0.1.1_EasyOcrEngine/blob/main/log_train.txt
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options: https://huggingface.co/fimu-docproc-research/standard_0.1.1_EasyOcrEngine/blob/main/opt.txt
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```python
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>>> import easyocr
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>>> import torch
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>>> from huggingface_hub import hf_hub_download
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>>> # Initialize default easyocr model
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>>> reader = easyocr.Reader(['en', 'cs', 'sk', 'pl'])
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>>> # Download weights of recognition module.
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>>> model_dir = hf_hub_download(repo_id="fimu-docproc-research/standard_0.1.1_EasyOcrEngine", filename="weights.pth")
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>>> # Load the weights
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>>> state_dict = torch.load(model_dir, map_location="cuda")
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>>> # Load the state dictionary into the model
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>>> reader.recognizer.load_state_dict(state_dict)
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>>> # Typical usage of easyocr model to get predictions
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>>> res = reader.readtext(input_img)
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```
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### Example usage (without GPU):
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```python
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>>> from collections import OrderedDict
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>>> import easyocr
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>>> import torch
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>>> from huggingface_hub import hf_hub_download
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>>> # Initialize default easyocr model
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>>> reader = easyocr.Reader(['en', 'cs', 'sk', 'pl'], quantize=False, gpu=False)
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>>> # Download weights of recognition module.
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>>> model_dir = hf_hub_download(repo_id="fimu-docproc-research/standard_0.1.1_EasyOcrEngine", filename="weights.pth")
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>>> # Load the weights
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>>> state_dict = torch.load(model_dir, map_location="cpu")
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>>> # There is need to remove first 7 characters due to easyocr library
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>>> new_state_dict = OrderedDict()
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>>> for key, value in state_dict.items():
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>>> new_key = key[7:]
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>>> new_state_dict[new_key] = value
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>>> # Load the state dictionary into the model
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>>> reader.recognizer.load_state_dict(new_state_dict)
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>>> # Typical usage of easyocr model to get predictions
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>>> res = reader.readtext(input_img)
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```
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