rare doc and opt post_process
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@ -40,7 +40,7 @@ PaddleOCR基于动态图开源的文本识别算法列表：
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- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐）
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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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- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
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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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@ -53,6 +53,9 @@ 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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|RARE|MobileNetV3|82.5|rec_mv3_tps_bilstm_att||[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
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|RARE|Resnet34_vd|83.6|rec_r34_vd_tps_bilstm_att||[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_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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@ -201,6 +201,8 @@ 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_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
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| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
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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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@ -42,7 +42,7 @@ PaddleOCR open-source text recognition algorithms list:
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- [x] CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
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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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- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
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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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@ -55,6 +55,8 @@ 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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|RARE|MobileNetV3|82.5|rec_mv3_tps_bilstm_att||[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
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|RARE|Resnet34_vd|83.6|rec_r34_vd_tps_bilstm_att||[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_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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@ -195,8 +195,11 @@ 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_mv3_tps_bilstm_att.yml | CRNN | Mobilenet_v3 | TPS | BiLSTM | att |
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| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
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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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co
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@ -143,6 +143,35 @@ class AttnLabelDecode(BaseRecLabelDecode):
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dict_character = [self.beg_str] + dict_character + [self.end_str]
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return dict_character
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def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
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""" convert text-index into text-label. """
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result_list = []
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ignored_tokens = self.get_ignored_tokens()
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[beg_idx, end_idx] = self.get_ignored_tokens()
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batch_size = len(text_index)
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for batch_idx in range(batch_size):
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char_list = []
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conf_list = []
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for idx in range(len(text_index[batch_idx])):
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if text_index[batch_idx][idx] in ignored_tokens:
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continue
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if int(text_index[batch_idx][idx]) == int(end_idx):
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break
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if is_remove_duplicate:
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# only for predict
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if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
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batch_idx][idx]:
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continue
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char_list.append(self.character[int(text_index[batch_idx][
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idx])])
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if text_prob is not None:
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conf_list.append(text_prob[batch_idx][idx])
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else:
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conf_list.append(1)
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text = ''.join(char_list)
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result_list.append((text, np.mean(conf_list)))
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return result_list
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def __call__(self, preds, label=None, *args, **kwargs):
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"""
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text = self.decode(text)
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@ -157,10 +186,10 @@ class AttnLabelDecode(BaseRecLabelDecode):
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preds_idx = preds.argmax(axis=2)
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preds_prob = preds.max(axis=2)
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text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
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text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
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if label is None:
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return text
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label = self.decode(label, is_remove_duplicate=True)
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label = self.decode(label, is_remove_duplicate=False)
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return text, label
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def encoder(self, labels, labels_length):
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@ -226,12 +255,12 @@ class SRNLabelDecode(BaseRecLabelDecode):
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text = self.decode(preds_idx, preds_prob)
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if label is None:
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text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
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text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
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return text
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label = self.decode(label)
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return text, label
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def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
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def decode(self, text_index, text_prob=None, is_remove_duplicate=True):
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""" convert text-index into text-label. """
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result_list = []
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ignored_tokens = self.get_ignored_tokens()
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