add srn doc
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@ -41,7 +41,7 @@ PaddleOCR基于动态图开源的文本识别算法列表：
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- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
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- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
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- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
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- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
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- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
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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：
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@ -53,5 +53,5 @@ PaddleOCR基于动态图开源的文本识别算法列表：
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|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)|
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|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)|
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|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)|
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|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn | [下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar) |
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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)。
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@ -22,8 +22,9 @@ inference 模型（`paddle.jit.save`保存的模型）
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- [三、文本识别模型推理](#文本识别模型推理)
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- [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
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- [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
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- [3. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
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- [4. 多语言模型的推理](#多语言模型的推理)
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- [3. 基于SRN损失的识别模型推理](#基于SRN损失的识别模型推理)
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- [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
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- [5. 多语言模型的推理](#多语言模型的推理)
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- [四、方向分类模型推理](#方向识别模型推理)
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- [1. 方向分类模型推理](#方向分类模型推理)
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@ -295,8 +296,20 @@ Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
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self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
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dict_character = list(self.character_str)
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```
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<a name="基于SRN损失的识别模型推理"></a>
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### 3. 基于SRN损失的识别模型推理
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基于SRN损失的识别模型，需要额外设置识别算法参数 --rec_algorithm="SRN"。
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同时需要保证预测shape与训练时一致，如： --rec_image_shape="1, 64, 256"
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### 3. 自定义文本识别字典的推理
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```
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
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--rec_model_dir="./inference/srn/" \
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--rec_image_shape="1, 64, 256" \
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--rec_char_type="en" \
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--rec_algorithm="SRN"
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```
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### 4. 自定义文本识别字典的推理
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如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径，并且设置 `rec_char_type=ch`
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```
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@ -304,7 +317,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
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```
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<a name="多语言模型的推理"></a>
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### 4. 多语言模型的推理
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### 5. 多语言模型的推理
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如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，
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需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
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@ -36,6 +36,7 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
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* 数据下载
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若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，下载 benchmark 所需的lmdb格式数据集。
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如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。
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<a name="自定义数据集"></a>
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* 使用自己数据集
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@ -200,6 +201,7 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
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| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
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| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
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| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
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| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
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训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
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@ -43,7 +43,7 @@ PaddleOCR open-source text recognition algorithms list:
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- [x] Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
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- [x] STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
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- [ ] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
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- [ ] SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon
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- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
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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:
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@ -55,5 +55,5 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
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|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)|
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|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)|
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|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)|
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|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
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Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./recognition_en.md)
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@ -25,6 +25,7 @@ Next, we first introduce how to convert a trained model into an inference model,
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- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
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- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
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- [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
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- [3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION)
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- [3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
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- [4. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
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@ -304,8 +305,23 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
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dict_character = list(self.character_str)
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```
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<a name="SRN-BASED_RECOGNITION"></a>
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### 3. SRN-BASED TEXT RECOGNITION MODEL INFERENCE
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The recognition model based on SRN requires additional setting of the recognition algorithm parameter
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--rec_algorithm="SRN". At the same time, it is necessary to ensure that the predicted shape is consistent
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with the training, such as: --rec_image_shape="1, 64, 256"
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```
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python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
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--rec_model_dir="./inference/srn/" \
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--rec_image_shape="1, 64, 256" \
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--rec_char_type="en" \
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--rec_algorithm="SRN"
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```
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<a name="USING_CUSTOM_CHARACTERS"></a>
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### 3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
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### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
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If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
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```
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@ -313,7 +329,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
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```
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<a name="MULTILINGUAL_MODEL_INFERENCE"></a>
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### 4. MULTILINGAUL MODEL INFERENCE
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### 5. MULTILINGAUL MODEL INFERENCE
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If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
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You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
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@ -195,6 +195,7 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
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| rec_mv3_none_none_ctc.yml | Rosetta | Mobilenet_v3 large 0.5 | None | None | ctc |
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| rec_r34_vd_none_bilstm_ctc.yml | CRNN | Resnet34_vd | None | BiLSTM | ctc |
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| rec_r34_vd_none_none_ctc.yml | Rosetta | Resnet34_vd | None | None | ctc |
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| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
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For training Chinese data, it is recommended to use
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[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
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