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license: mit |
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# Document Scanner :bookmark_tabs: |
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[U-Net](https://arxiv.org/abs/1505.04597v1) Like Pretrained Model For Scene Document Detection ([pytorch](https://pytorch.org/), [Semantic Segmentation](https://paperswithcode.com/task/semantic-segmentation)) |
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#### **Quick Links** |
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- [Dependencies](#Dependencies) |
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- [Usage](#Usage) |
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- [Examples](#Examples) |
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## Dependencies |
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- Install Dependencies `$ pip install -r requirements.txt` |
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- Download model weights [Here](), place it in `Structure/` |
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## Usage: |
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```python |
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scanner = Scanner("Structure/Scanner-Detector.pth", config_) |
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``` |
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Load model. |
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```python |
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org = cv2.imread(fname) |
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org_gray = cv2.cvtColor(org, cv2.COLOR_RGB2GRAY) |
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org_resize = cv2.resize(org_gray, (256, 256), interpolation = cv2.INTER_AREA) |
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``` |
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Read image in gray scale and resize it to 256*256. |
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```python |
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mask = scanner.ScanView(org_resize) |
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``` |
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Detect document area. |
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```python |
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paper, approx = ExtractPaper(org_gray, mask) |
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org = DrawBox(org, approx) |
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``` |
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Extract document and draw bounding box on original image. |
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```python |
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paper = EnhacePaper(paper) |
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``` |
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Enhance extracted document. |
