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README.md
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license: mit
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---
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---
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license: mit
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---
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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.
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## Examples
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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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### 🛡️ 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)
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