# SEED - [1. Introduction](#1) - [2. Environment](#2) - [3. Model Training / Evaluation / Prediction](#3) - [3.1 Training](#3-1) - [3.2 Evaluation](#3-2) - [3.3 Prediction](#3-3) - [4. Inference and Deployment](#4) - [4.1 Python Inference](#4-1) - [4.2 C++ Inference](#4-2) - [4.3 Serving](#4-3) - [4.4 More](#4-4) - [5. FAQ](#5) ## 1. Introduction Paper: > [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://arxiv.org/pdf/2005.10977.pdf) > Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping > CVPR, 2020 Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows: |Model|Backbone|ACC|config|Download link| | --- | --- | --- | --- | --- | |SEED|Aster_Resnet| 85.2% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) | ## 2. Environment Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code. ## 3. Model Training / Evaluation / Prediction Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**. Training: The SEED model needs to additionally load the [language model](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz) trained by FastText, and install the fasttext dependencies: ``` python3 -m pip install fasttext==0.9.1 ``` Specifically, after the data preparation is completed, the training can be started. The training command is as follows: ``` #Single GPU training (long training period, not recommended) python3 tools/train.py -c configs/rec/rec_resnet_stn_bilstm_att.yml #Multi GPU training, specify the gpu number through the --gpus parameter python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c rec_resnet_stn_bilstm_att.yml ``` Evaluation: ``` # GPU evaluation python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy ``` Prediction: ``` # The configuration file used for prediction must match the training python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png ``` ## 4. Inference and Deployment ### 4.1 Python Inference First, the model saved during the SEED text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) ), you can use the following command to convert: ``` python3 tools/export_model.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=seed_infer ``` For SEED text recognition model inference, the following commands can be executed: ``` python3 tools/infer/predict_rec.py --rec_model_dir=seed_infer --image_dir=doc/imgs_words_en/word_10.png --rec_algorithm="SEED" --rec_char_dict_path=ppocr/utils/EN_symbol_dict.txt --rec_image_shape="3,64,256" --use_space_char=False ``` ### 4.2 C++ Inference Not support ### 4.3 Serving Not support ### 4.4 More Not support ## 5. FAQ ## Citation ```bibtex @inproceedings{qiao2020seed, title={Seed: Semantics enhanced encoder-decoder framework for scene text recognition}, author={Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={13528--13537}, year={2020} } ```