diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md index 8cebce3adf5c414674d2990c1b2a018ae52e57f6..f1c69b1de075b85780c954728fef83fbc1442052 100755 --- a/doc/doc_ch/algorithm_overview.md +++ b/doc/doc_ch/algorithm_overview.md @@ -40,17 +40,19 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训 PaddleOCR基于动态图开源的文本识别算法列表： - [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐） - [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10] -- [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] coming soon +- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] - [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon - [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon 参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下： |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接| -|-|-|-|-|-| +|---|---|---|---|---| |Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)| |Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)| |CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)| |CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)| +|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)| +|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)| PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。 diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md index c69c127aeabe14426b366426cfa3e2f90687c8be..ab5487037e69d40e38dde96fc8006022054f31df 100755 --- a/doc/doc_ch/inference.md +++ b/doc/doc_ch/inference.md @@ -352,10 +352,10 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982] ``` # 使用方向分类器 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true # 不使用方向分类器 -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false ``` @@ -364,7 +364,7 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model 执行命令后，识别结果图像如下： -![](../imgs_results/2.jpg) +![](../imgs_results/system_res_00018069.jpg) ### 2. 其他模型推理 @@ -381,4 +381,4 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --d 执行命令后，识别结果图像如下： -(coming soon) +![](../imgs_results/img_10_east_starnet.jpg) diff --git a/doc/doc_en/algorithm_overview_en.md b/doc/doc_en/algorithm_overview_en.md index f2349a1c3cb5096db23ff2a4465c51e0abfca36b..f2da14a48c2df0a6eefe253a735fb40f4de201c6 100755 --- a/doc/doc_en/algorithm_overview_en.md +++ b/doc/doc_en/algorithm_overview_en.md @@ -41,17 +41,19 @@ For the training guide and use of PaddleOCR text detection algorithms, please re PaddleOCR open-source text recognition algorithms list: - [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7] - [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10] -- [ ] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] coming soon +- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] - [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon - [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow: |Model|Backbone|Avg Accuracy|Module combination|Download link| -|-|-|-|-|-| +|---|---|---|---|---| |Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)| |Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)| |CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)| |CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)| +|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)| +|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)| Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./recognition_en.md) diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md index 8742b7ceeb4dc504da4f8d9344e489270a4b48bb..98e3ef6378480022baaf6e82843294dab3fbcaf4 100755 --- a/doc/doc_en/inference_en.md +++ b/doc/doc_en/inference_en.md @@ -366,15 +366,15 @@ When performing prediction, you need to specify the path of a single image or a ``` # use direction classifier -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true # not use use direction classifier -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" ``` After executing the command, the recognition result image is as follows: -![](../imgs_results/2.jpg) +![](../imgs_results/system_res_00018069.jpg) ### 2. OTHER MODELS @@ -391,4 +391,4 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --d After executing the command, the recognition result image is as follows: -(coming soon) +![](../imgs_results/img_10_east_starnet.jpg) diff --git a/doc/imgs_results/img_10_east_starnet.jpg b/doc/imgs_results/img_10_east_starnet.jpg new file mode 100644 index 0000000000000000000000000000000000000000..fd8c039230dfd9935472f644ee90c6ca442a362d Binary files /dev/null and b/doc/imgs_results/img_10_east_starnet.jpg differ diff --git a/doc/imgs_results/system_res_00018069.jpg b/doc/imgs_results/system_res_00018069.jpg new file mode 100644 index 0000000000000000000000000000000000000000..fc06b05085e374aa5c82aad4173c245583ef6089 Binary files /dev/null and b/doc/imgs_results/system_res_00018069.jpg differ diff --git a/ppocr/modeling/transforms/tps.py b/ppocr/modeling/transforms/tps.py index 3de25193d6bf031c9cac2d026c5031ce4bb511fd..78338edf67d69e32322912d75dec01ce1e63cb49 100644 --- a/ppocr/modeling/transforms/tps.py +++ b/ppocr/modeling/transforms/tps.py @@ -213,16 +213,14 @@ class GridGenerator(nn.Layer): def build_P_paddle(self, I_r_size): I_r_height, I_r_width = I_r_size - I_r_grid_x = paddle.divide( - paddle.arange( - -I_r_width, I_r_width, 2, dtype='float64') + 1.0, - paddle.to_tensor( - I_r_width, dtype='float64')) - I_r_grid_y = paddle.divide( - paddle.arange( - -I_r_height, I_r_height, 2, dtype='float64') + 1.0, - paddle.to_tensor( - I_r_height, dtype='float64')) # self.I_r_height + I_r_grid_x = (paddle.arange( + -I_r_width, I_r_width, 2, dtype='float64') + 1.0 + ) / paddle.to_tensor(np.array([I_r_width])) + + I_r_grid_y = (paddle.arange( + -I_r_height, I_r_height, 2, dtype='float64') + 1.0 + ) / paddle.to_tensor(np.array([I_r_height])) + # P: self.I_r_width x self.I_r_height x 2 P = paddle.stack(paddle.meshgrid(I_r_grid_x, I_r_grid_y), axis=2) P = paddle.transpose(P, perm=[1, 0, 2]) diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index 1c72863c7d448f536da38d7dd19e6dca639803c1..4d078994ad6b0020280b8a7ec5eec3626e7075cc 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -109,7 +109,7 @@ class CTCLabelDecode(BaseRecLabelDecode): preds_idx = preds.argmax(axis=2) preds_prob = preds.max(axis=2) - text = self.decode(preds_idx, preds_prob) + text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True) if label is None: return text label = self.decode(label)