文本检测-基于Pytorch实现CRAFT文本检测算法-附项目源码-优质项目实战.zip

## CRAFT: Character-Region Awareness For Text detection ### Overview PyTorch implementation for CRAFT text detector that effectively detect text area by exploring each character region and affinity between characters. The bounding box of texts are obtained by simply finding minimum bounding rectangles on binary map after thresholding character region and affinity scores. <img width="1000" alt="teaser" src="./figures/craft_example.gif"> ## Getting started ### Install dependencies #### Requirements - PyTorch>=0.4.1 - torchvision>=0.2.1 - opencv-python>=3.4.2 - check requiremtns.txt ``` pip install -r requirements.txt ``` ### Training The code for training is not included in this repository, and we cannot release the full training code for IP reason. ### Test instruction using pretrained model - Download the trained models *Model name* | *Used datasets* | *Languages* | *Purpose* | *Model Link* | | :--- | :--- | :--- | :--- | :--- | General | SynthText, IC13, IC17 | Eng + MLT | For general purpose | [Click](https://drive.google.com/open?id=1Jk4eGD7crsqCCg9C9VjCLkMN3ze8kutZ) IC15 | SynthText, IC15 | Eng | For IC15 only | [Click](https://drive.google.com/open?id=1i2R7UIUqmkUtF0jv_3MXTqmQ_9wuAnLf) LinkRefiner | CTW1500 | - | Used with the General Model | [Click](https://drive.google.com/open?id=1XSaFwBkOaFOdtk4Ane3DFyJGPRw6v5bO) * Run with pretrained model ``` (with python 3.7) python test.py --trained_model=[weightfile] --test_folder=[folder path to test images] ``` The result image and socre maps will be saved to `./result` by default. ### Arguments * `--trained_model`: pretrained model * `--text_threshold`: text confidence threshold * `--low_text`: text low-bound score * `--link_threshold`: link confidence threshold * `--cuda`: use cuda for inference (default:True) * `--canvas_size`: max image size for inference * `--mag_ratio`: image magnification ratio * `--poly`: enable polygon type result * `--show_time`: show processing time * `--test_folder`: folder path to input images * `--refine`: use link refiner for sentense-level dataset * `--refiner_model`: pretrained refiner model