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deep-text-recognition-benchmark
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# What is wrong with scene text recognition model comparisons? dataset and model analysis
| [paper](https://arxiv.org/abs/1904.01906) | [download training and evaluation data](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) | [download failure cases and cleansed label](https://github.com/clovaai/deep-text-recognition-benchmark#download-failure-cases-and-cleansed-label-from-here) |
| [paper](https://arxiv.org/abs/1904.01906) | [download training and evaluation data](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) | [download failure cases and cleansed label](https://github.com/clovaai/deep-text-recognition-benchmark#download-failure-cases-and-cleansed-label-from-here) | [download pretrained model](https://drive.google.com/drive/folders/15WPsuPJDCzhp2SvYZLRj8mAlT3zmoAMW) |
PyTorch implementation of our four-stage STR framework, that most existing STR models fit into.
Using this framework allows for the module-wise contributions to performance in terms of accuracy, speed, and memory demand, under one consistent set of training and evaluation datasets.
@ -37,7 +37,7 @@ CUDA_VISIBLE_DEVICES=0 python3 test.py \
--Transformation None --FeatureExtraction VGG --SequenceModeling BiLSTM --Prediction CTC \
--saved_model saved_models/None-VGG-BiLSTM-CTC-Seed1111/best_accuracy.pth
```
3. Try to train and test our best accuracy combination (TPS-ResNet-BiLSTM-Attn) also.
3. Try to train and test our best accuracy combination (TPS-ResNet-BiLSTM-Attn) also. ([download pretrained model](https://drive.google.com/drive/folders/15WPsuPJDCzhp2SvYZLRj8mAlT3zmoAMW))
```
CUDA_VISIBLE_DEVICES=0 python3 train.py \
--train_data data_lmdb_release/training --valid_data data_lmdb_release/validation \