From 2c04b4ad9534734a68cc34cb5f1099e53533e0a3 Mon Sep 17 00:00:00 2001 From: MissPenguin Date: Tue, 9 Jun 2020 20:50:35 +0800 Subject: [PATCH] Update README_en.md --- README_en.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README_en.md b/README_en.md index 0a4c243d..a3f95e02 100644 --- a/README_en.md +++ b/README_en.md @@ -115,7 +115,7 @@ On the ICDAR2015 dataset, the text detection result is as follows: |DB|ResNet50_vd|83.79%|80.65%|82.19%|[Download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)| |DB|MobileNetV3|75.92%|73.18%|74.53%|[Download link](https://paddleocr.bj.bcebos.com/det_mv3_db.tar)| -For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/datasets.md#1icdar2019-lsvt) street view dataset with a total of 3w training data，the related configuration and pre-trained models for Chinese detection task are as follows: +For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) street view dataset with a total of 3w training data，the related configuration and pre-trained models for Chinese detection task are as follows: |Model|Backbone|Configuration file|Pre-trained model| |-|-|-|-| |Ultra-lightweight Chinese model|MobileNetV3|det_mv3_db.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar)| @@ -123,7 +123,7 @@ For use of [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/dat * Note: For the training and evaluation of the above DB model, post-processing parameters box_thresh=0.6 and unclip_ratio=1.5 need to be set. If using different datasets and different models for training, these two parameters can be adjusted for better result. -For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./doc/doc_en/detection.md) +For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./doc/doc_en/detection_en.) ## Text recognition algorithm @@ -147,13 +147,13 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r |RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)| |RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[Download link](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)| -We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/datasets.md#1icdar2019-lsvt) dataset and cropout 30w traning data from original photos by using position groundtruth and make some calibration needed. In addition, based on the LSVT corpus, 500w synthetic data is generated to train the Chinese model. The related configuration and pre-trained models are as follows: +We use [LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/datasets_en.md#1-icdar2019-lsvt) dataset and cropout 30w traning data from original photos by using position groundtruth and make some calibration needed. In addition, based on the LSVT corpus, 500w synthetic data is generated to train the Chinese model. The related configuration and pre-trained models are as follows: |Model|Backbone|Configuration file|Pre-trained model| |-|-|-|-| |Ultra-lightweight Chinese model|MobileNetV3|rec_chinese_lite_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar)| |General Chinese OCR model|Resnet34_vd|rec_chinese_common_train.yml|[Download link](https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn.tar)| -Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/recognition.md) +Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md) ## End-to-end OCR algorithm - [ ] [End2End-PSL](https://arxiv.org/abs/1909.07808)(Baidu Self-Research, comming soon) -- GitLab