path=../ppocr/utils/dict/table_structure_dict.txt \ --det_limit_side_len=736 \ --det_limit_type=min \ --rec_image_shape=3,32,320 \ --gt_path=path/to/gt.txt 将会输出 teds: 95.89 5. Reference https://github.com/ibm-aur-nlp/PubTabNet https://arxiv.org/pdf/1911.10683
PP-Structure是PaddleOCR团队自研的智能文档分析系统,旨在帮助开发者更好的完成版面分析、表格识别等文档理解相关任务。 PP-StructureV2系统流程图如下所示,文档图像首先经过图像矫正模块,判断整图方向并完成转正,随后可以完成版面信息分析与关键信息抽取2类任务。 版面分析任务中,图像首先经过版面分析模型,将图像划分为文本...
.github applications benchmark configs deploy doc ppocr ppstructure docs imgs kie layout recovery table PP-StructureV2_introduction.md inference.md inference_en.md models_list.md models_list_en.md quickstart.md quickstart_en.md kie layout pdf2word recovery table README.md README_ch.md __init_...
.github applications benchmark configs deploy doc ppocr ppstructure docs imgs kie layout recovery table PP-StructureV2_introduction.md inference.md inference_en.md models_list.md models_list_en.md quickstart.md quickstart_en.md kie layout
.github applications benchmark configs deploy doc ppocr ppstructure docs imgs kie layout recovery table PP-StructureV2_introduction.md inference.md inference_en.md models_list.md models_list_en.md quickstart.md quickstart_en.md kie layout
.github applications benchmark configs deploy doc ppocr ppstructure docs kie layout pdf2word recovery table README.md README_ch.md __init__.py predict_system.py return_word_pos.md utility.py test_tipc tests tools .clang_format.hook .gitignore .pre-commit-config.yaml .style.yapf LICENSE MAN...
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这里选用PP-StructureV2中的表格识别模型[SLANet](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/configs/table/SLANet.yml) 196 196 197 - SLANet是PP-Structurev2全新推出的表格识别模型,相比PP-Structurev1中TableRec-RARE,在速度不变的情况下精度提升4.7%。TEDS提升2% 197 + SLANet是PP-StructureV2...
.github PPOCRLabel StyleText applications benchmark configs deploy doc ppocr ppstructure docs kie layout pdf2word recovery table README.md README_ch.md __init__.py predict_system.py return_word_pos.md utility.py test_tipc tools .clang_format.hook .gitignore .pre-commit-con...
版本、环境信息: 1)PaddlePaddle版本:release/2.2 2)ARM设备: NVIDIA Jetson AGX Xavier 3)系统环境:JetPack 4.6, Python版本 3.8 版本 于docker内做编译 docker image: nvcr.io/nvidia/l4t-ml:r32.6.1-py3 Python 3.8 操作流程参照此篇文章 https://www.codeleading.