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Add wildreceipt dataset #1359
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HamzaGbada:add-wildreceipt-dataset
Oct 27, 2023
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Add wildreceipt dataset #1359
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7ae18dc
[ADD] wildreceipt init
HamzaGbada f4c4895
[ADD] wildreceipt init
HamzaGbada a883ed0
[ADD] wildreceipt _convert_xmin_ymin
HamzaGbada ddb4d67
[ADD] wildreceipt _convert_xmin_ymin
HamzaGbada 15abe0d
[ADD] wildreceipt _convert_xmin_ymin
HamzaGbada dcb63cb
[ADD] wildreceipt test
HamzaGbada 87bf015
[ADD] wildreceipt test
HamzaGbada 17c1112
[UPDATE] wildreceipt use_polygon
HamzaGbada f197337
[UPDATE] wildreceipt img_folder
HamzaGbada 3c7ce8d
[ADD] mock_wildreceipt_dataset in conftest.py
HamzaGbada b7d8cb7
[BUG] mock_wildreceipt_dataset in conftest.py
HamzaGbada a1f09b0
[BUG] mock_wildreceipt_dataset in conftest.py
HamzaGbada e3b9bdc
[BUG] mock_wildreceipt_dataset in conftest.py
HamzaGbada 8c57b75
[BUG] mock_wildreceipt_dataset in conftest.py
HamzaGbada 630437d
[BUG] mock_wildreceipt_dataset in conftest.py
HamzaGbada 15804df
[BUG] mock_wildreceipt_dataset in conftest.py
HamzaGbada 275afa5
[FIX] mock_wildreceipt_dataset labels
HamzaGbada 82ed210
[FIX] mock_wildreceipt_dataset labels
HamzaGbada 1e06371
[FIX] mock_wildreceipt_dataset labels
HamzaGbada a968db4
remove todos
HamzaGbada e42c71e
remove todos
HamzaGbada 4ec3bf5
[UPDATE] wildreceipt_image_folder
HamzaGbada ff4b399
[ADD] test_wildreceipt_dataset tf
HamzaGbada 2a7d1e0
[UPDATE] WILDRECEIPT optimize imports
HamzaGbada bffca24
[FIX] WILDRECEIPT self.data
HamzaGbada 954b8b0
[UPDATE] save fata in RAM
HamzaGbada edbcaf2
[UPDATE] docs
HamzaGbada e257a29
[UPDATE] box wildreceipt
HamzaGbada 2b3a578
[UPDATE] docs
HamzaGbada c18175b
[UPDATE] filter empty and whitespace
HamzaGbada 6c33799
[UPDATE] filter empty and whitespace
HamzaGbada fcedaba
[FIX] format
HamzaGbada 478a420
[FIX] format
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Original file line number | Diff line number | Diff line change |
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|
@@ -99,7 +99,7 @@ def __init__( | |
img_path=os.path.join(tmp_root, img_path), geoms=np.asarray(box_targets, dtype=int).clip(min=0) | ||
) | ||
for crop, label in zip(crops, list(text_targets)): | ||
if not any(char in label for char in ["", "-", "*", "/", "=", "#", "@"]): | ||
if not any(char in label for char in ["", " "]): | ||
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|
||
self.data.append((crop, label)) | ||
else: | ||
self.data.append( | ||
|
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Do you know if there are text inside we need to filter out ?
For example text which contains whitespaces ?
Ref.:
doctr/doctr/datasets/funsd.py
Line 100 in f22f6dd
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It's worth noting that this dataset contains small text elements that might not be conducive to the recognition task. For instance, we could consider filtering out text elements that are empty or consist of characters such as
"-", "*", "/", "=", "#", or "@"
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@HamzaGbada
Mh in this case i think it would be enough to filter empty elements or if a whitespace is in the label.
We can handle all the above punctuations :)