Create README.md

README.md

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+```python
+>>> import easyocr
+>>> import torch
+>>> from huggingface_hub import hf_hub_download
+
+>>> # Initialize default easyocr model
+>>> reader = easyocr.Reader(['en', 'cs', 'sk', 'pl'])
+>>> # Download weights of recognition module.
+>>> model_dir = hf_hub_download(repo_id="fimu-docproc-research/standard_0.4.0_EasyOcrEngine", filename="weights.pth")
+>>> # Load the weights
+>>> state_dict = torch.load(model_dir, map_location="cuda")
+>>> # Load the state dictionary into the model
+>>> reader.recognizer.load_state_dict(state_dict)
+
+>>> # Typical usage of easyocr model  to get predictions
+>>> res = reader.readtext(input_img)
+```
+
+### Example usage (without GPU):
+
+```python
+>>> from collections import OrderedDict
+
+>>> import easyocr
+>>> import torch
+>>> from huggingface_hub import hf_hub_download
+
+>>> # Initialize default easyocr model
+>>> reader = easyocr.Reader(['en', 'cs', 'sk', 'pl'], quantize=False, gpu=False)
+>>> # Download weights of recognition module.
+>>> model_dir = hf_hub_download(repo_id="fimu-docproc-research/standard_0.4.0_EasyOcrEngine", filename="weights.pth")
+>>> # Load the weights
+>>> state_dict = torch.load(model_dir, map_location="cpu")
+>>> # There is need to remove first 7 characters due to easyocr library
+>>> new_state_dict = OrderedDict()
+>>> for key, value in state_dict.items():
+>>>     new_key = key[7:]
+>>>     new_state_dict[new_key] = value
+
+>>> # Load the state dictionary into the model
+>>> reader.recognizer.load_state_dict(new_state_dict)
+
+>>> # Typical usage of easyocr model  to get predictions
+>>> res = reader.readtext(input_img)
+```