multi-task learning for text recognition with joint ctc-attention.
- support variable length of squence training and inference (fixed height).
- Chinese character recognition.
- Joint CTC-Attention
- This work was tested with PyTorch 1.1.0, CUDA 9.0, python 3.6 and centos7
- requirements : pytorch, lmdb, pillow, torchvision, nltk, natsort
pip3 install torch==1.1.0
pip3 install lmdb pillow torchvision nltk natsort
- Download pretrained model(crnn) from baidu code:un8d
pretrained crnn model configuration:
--output_channel 512 \
--hidden_size 256 \
--Transformation None \
--FeatureExtraction ResNet \
--SequenceModeling BiLSTM \
--Prediction CTC
run demo with pretrained mdoel.
# change config.py file and run:
CUDA_VISIBLE_DEVICES=0 python3 infer.py ${image_path}
- CTC-Attention pretrained model will release later.
| demo images | None-ResNet512-BiLSTM256-CTC | None-ResNet768-BiLSTM384-CTC |
|---|---|---|
 |
同达电动车配件 | 同达电动车配件 |
 |
微信14987227 | 微信14987227 |
 |
快乐大本营20190629期:张艺兴李荣浩惊喜同台合唱彭昱畅破音三连引 | 快乐大本营20190629期:张艺兴李荣浩惊喜同台合唱彭昱畅破音三连引 |
 |
每周三中午12:00 | 每周三中午12-00 |
 |
整套征兵甄别程序的一个部分 | 整套征兵甄别程序的一个部分 |
 |
再热烈的鼓掌 | 再热烈的鼓掌 |
 |
厂外恒升拆车件 | 广州恒升拆车件 |
 |
我想说你为什么 | 我想说你为什么 |
 |
我抱吧他在你怀里一直在哭 | 我抱吧他在你怀里一直在哭 |
 |
如果没有这个阿姨的话 | 如果没有这个阿姨的话 |
 |
因为我觉得有你了我才有安全感 | 因为我觉得有你了我才有安全感 |
 |
丫OUI<U | YOUKU |
 |
麦油娜孕其智商下线 | 轰迪娜马期智商下线 |
 |
因为后一仁看《《二》的巡演 | 团为册长加看飞广海》的巡演 |
 |
铂爵 | 铂爵 |
 |
婆婆担心张晋怒斥蔡少芬 | 婆婆担心张晋怒斥蔡少芬 |
 |
若风作为队长带队获得/PL5总冠军 | 若风作为队长带队获得/PL5总冠军 |
 |
向佐向郭碧婷求婚戊功 | 向佐向郭碧婷求婚成功 |
 |
严屹宽骨亲 | 严屹宽母宗 |
 |
哇品会 | 唯品会 |
 |
YOUI<U独播 | YOUKU独播 |
- Train CRNN model
CUDA_VISIBLE_DEVICES=0 python train.py \
--train_data data/synch/lmdb_train \
--valid_data data/synch/lmdb_val \
--select_data / --batch_ratio 1 \
--sensitive \
--num_iter 400000 \
--output_channel 512 \
--hidden_size 256 \
--Transformation None \
--FeatureExtraction ResNet \
--SequenceModeling BiLSTM \
--Prediction CTC \
--experiment_name none_resnet_bilstm_ctc \
--continue_model saved_models/pretrained_model.pth
- Train CTC-Attention model
CUDA_VISIBLE_DEVICES=0 python train.py \
--train_data data/synch/lmdb_train \
--valid_data data/synch/lmdb_val \
--select_data / --batch_ratio 1 \
--sensitive \
--num_iter 400000 \
--output_channel 512 \
--hidden_size 256 \
--Transformation None \
--FeatureExtraction ResNet \
--SequenceModeling BiLSTM \
--Prediction CTC \
--mtl \
--without_prediction \
--experiment_name none_resnet_bilstm_ctc \
--continue_model saved_models/pretrained_model.pth
- This implementation has mainly been based on this great repository: deep-text-recognition-benchmark
- SynthText Generation has mainly been based on TextRecognitionDataGenerator
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