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  license: mit
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ ```python
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+ paper = EnhacePaper(paper)
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+ ```
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+ Enhance extracted document.
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+
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+ ## Examples
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+
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+ ![](Images/1704353164820_det.jpg)
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+ ![](Images/1704387464271_det.jpg)
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+ ![](Images/1704640185357_det.jpg)
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+ ![](Images/1704721611426_det.jpg)
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+ ![](Images/1705745193148_det.jpg)
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+
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+
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+ ### 🛡️ License <a name="license"></a>
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+ Project is distributed under [MIT License](https://github.com/Saeed-Biabani/Document-Scanner/blob/main/LICENSE)