| --- |
| license: mit |
| --- |
| # Document Scanner :bookmark_tabs: |
| [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)) |
| |
| #### **Quick Links** |
| - [Dependencies](#Dependencies) |
| - [Usage](#Usage) |
| - [Examples](#Examples) |
| |
| ## Dependencies |
| - Install Dependencies `$ pip install -r requirements.txt` |
| - Download model weights [Here](), place it in `Structure/` |
| |
| ## Usage: |
| ```python |
| scanner = Scanner("Structure/Scanner-Detector.pth", config_) |
| ``` |
| Load model. |
| |
| ```python |
| org = cv2.imread(fname) |
| org_gray = cv2.cvtColor(org, cv2.COLOR_RGB2GRAY) |
| org_resize = cv2.resize(org_gray, (256, 256), interpolation = cv2.INTER_AREA) |
| ``` |
| Read image in gray scale and resize it to 256*256. |
| |
| ```python |
| mask = scanner.ScanView(org_resize) |
| ``` |
| Detect document area. |
| |
| ```python |
| paper, approx = ExtractPaper(org_gray, mask) |
| org = DrawBox(org, approx) |
| ``` |
| Extract document and draw bounding box on original image. |
| |
| ```python |
| paper = EnhacePaper(paper) |
| ``` |
| Enhance extracted document. |
