CRAFT pytorch
1.0.0
Pytorch官方的手工藝文本檢測器實施|紙|預驗證的模型|補充
Youngmin Baek,Bado Lee,Dongyoon Han,Sangdoo Yun,Hwalsuk Lee。
Naver Corp. Clova AI研究
Pytorch實現工藝文本檢測器,該實現通過探索每個字符區域和字符之間的親和力來有效地檢測文本區域。通過簡單地在閾值字符區域和親和力分數之後簡單地在二進製圖上找到最小邊界矩形來獲得文本的邊界框。

2019年6月13日: 2019年7月20日最初更新:添加了Polygon結果的後處理,2019年9月28日:添加了IC15上的訓練有素的型號和鏈接煉油廠
pip install -r requirements.txt
該存儲庫中未包含培訓代碼,我們不能出於IP原因發布完整的培訓代碼。
| 模型名稱 | 使用的數據集 | 語言 | 目的 | 模型鏈接 |
|---|---|---|---|---|
| 一般的 | Synthtext,IC13,IC17 | ENG + MLT | 出於一般目的 | 點擊 |
| IC15 | Synthtext,IC15 | 工程 | 僅適用於IC15 | 點擊 |
| LinkRefiner | CTW1500 | - | 與通用模型一起使用 | 點擊 |
python test.py --trained_model=[weightfile] --test_folder=[folder path to test images]
默認情況下,結果圖像和SOCRE地圖將保存到./result 。
--trained_model :驗證的模型--text_threshold :文本置信度門檻--low_text :文本低結合分數--link_threshold :鏈接置信度閾值--cuda :使用CUDA進行推理(默認:true)--canvas_size :推理的最大圖像大小--mag_ratio :圖像放大率--poly :啟用多邊形類型結果--show_time :顯示處理時間--test_folder :輸入圖像的文件夾路徑--refine :使用鏈接煉油廠作為Sentense級數據集--refiner_model :預處理的煉油廠模型 @inproceedings{baek2019character,
title={Character Region Awareness for Text Detection},
author={Baek, Youngmin and Lee, Bado and Han, Dongyoon and Yun, Sangdoo and Lee, Hwalsuk},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={9365--9374},
year={2019}
}
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