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Save ttchengab/c2f7614cbeaa8cd14883d4ebbcd36ba6 to your computer and use it in GitHub Desktop.
| VOCAB= ascii_uppercase+digits+punctuation+" \t\n" | |
| #Change to CUDA to run using GPU | |
| device = 'cpu' | |
| def get_test_data(etfo): | |
| text = etfo | |
| text_tensor = torch.zeros(len(text), 1, dtype=torch.long) | |
| text_tensor[:, 0] = torch.LongTensor([VOCAB.find(c) for c in text]) | |
| return text_tensor.to(device) | |
| etfo = get_info('invoice.png') | |
| # etfo = get_info('X51005621482.jpeg') | |
| etfo = etfo.upper() | |
| text_tensor = get_test_data(etfo) | |
| temp = [] | |
| for i in range(len(text_tensor)): | |
| if text_tensor[i]>=0 and text_tensor[i]<=70: | |
| temp.append([int(text_tensor[i])]) | |
| text_tensor = torch.LongTensor(temp) | |
| #model initialization | |
| hidden_size = 256 | |
| device= torch.device('cpu') | |
| model = ExtractLSTM(len(VOCAB), 16, hidden_size).to(device) | |
| model.load_state_dict(torch.load('model.pth')) | |
| result = test(model) | |
| print(result) |
Hi, thank you for sharing this implementation code. I read your blog and I followed the steps correctly now I have the code running. However I think I chose the wrong model because I have poor results. I generated the 'model.pth' from this repo https://github.com/zzzDavid/ICDAR-2019-SROIE/tree/master/task3 please correct if I'm wrong and if not did you use different data that gave you these good results ?
I am facing the same issue that yasmineChelly-95 has.
I am getting very poor results.
Can you please let us know if you used any different data or something?
Hi, thank you for sharing this implementation code. I read your blog and I followed the steps correctly now I have the code running. However I think I chose the wrong model because I have poor results. I generated the 'model.pth' from this repo https://github.com/zzzDavid/ICDAR-2019-SROIE/tree/master/task3 please correct if I'm wrong and if not did you use different data that gave you these good results ?
Hello, could you share me your code so I could take a look at it?
Thank you
do we have any other pretained model to use in load method ?
I used the checkpoint from the solution I talked about in this blog:
https://nanonets.com/blog/key-value-pair-extraction-from-documents-using-ocr-and-deep-learning/