Table of Content

1. Introduction
2. Dataset
3. Getting Started
   • Requirements
   • Usage Example
4. Training & Evaluation
5. Acknowledgement

Dictionary-guided Scene Text Recognition

• We propose a novel dictionary-guided sense text recognition approach that could be used to improve many state-of-the-art models.
• We also introduce a new benchmark dataset (namely, VinText) for Vietnamese scene text recognition.

Comparison between the traditional approach and our proposed approach.

Details of the dataset construction, model architecture, and experimental results can be found in our following paper:

@inproceedings{m_Nguyen-etal-CVPR21,
      author = {Nguyen Nguyen and Thu Nguyen and Vinh Tran and Triet Tran and Thanh Ngo and Thien Nguyen and Minh Hoai},
      title = {Dictionary-guided Scene Text Recognition},
      year = {2021},
      booktitle = {Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
    }

Please CITE our paper whenever our dataset or model implementation is used to help produce published results or incorporated into other software.

Dataset

We introduce ✨ a new VinText dataset.

By downloading this dataset, USER agrees:

• to use this dataset for research or educational purposes only
• to not distribute or part of this dataset in any original or modified form.
• and to cite our paper whenever this dataset are employed to help produce published results.

Name	#imgs	#text instances	Examples
VinText	2000	About 56000

Detail about ✨ VinText dataset can be found in our paper. Download Converted dataset to try with our model

VinText

Extract data and copy folder to folder datasets/

datasets
└───vintext
	└───test.json
		│train.json
		|train_images
		|test_images
└───evaluation
	└───gt_vintext.zip

Getting Started

Requirements

• python=3.7
• torch==1.4.0
• detectron2==0.2

Installation

conda create -n dict-guided -y python=3.7
conda activate dict-guided
conda install -y pytorch torchvision cudatoolkit=10.0 -c pytorch
python -m pip install ninja yacs cython matplotlib tqdm opencv-python shapely scipy tensorboardX pyclipper Polygon3 weighted-levenshtein editdistance

# Install Detectron2
python -m pip install detectron2==0.2 -f \
  https://dl.fbaipublicfiles.com/detectron2/wheels/cu100/torch1.4/index.html

Check out the code and install:

git clone https://github.com/nguyennm1024/dict-guided.git
cd dict-guided
python setup.py build develop

Download vintext pre-trained model

• trained_model.

Usage

Prepare folders

mkdir sample_input
mkdir sample_output

Copy your images to sample_input/. Output images would result in sample_output/

python demo/demo.py --config-file configs/BAText/VinText/attn_R_50.yaml --input sample_input/ --output sample_output/ --opts MODEL.WEIGHTS path-to-trained_model-checkpoint

Qualitative Results on VinText.

Training and Evaluation

Training

For training, we employed the pre-trained model tt_attn_R_50 from the ABCNet repository for initialization.

python tools/train_net.py --config-file configs/BAText/VinText/attn_R_50.yaml MODEL.WEIGHTS path_to_tt_attn_R_50_checkpoint

Example:

python tools/train_net.py --config-file configs/BAText/VinText/attn_R_50.yaml MODEL.WEIGHTS ./tt_attn_R_50.pth

Trained model output will be saved in the folder output/batext/vintext/ that is then used for evaluation

Evaluation

python tools/train_net.py --eval-only --config-file configs/BAText/VinText/attn_R_50.yaml MODEL.WEIGHTS path_to_trained_model_checkpoint

Example:

python tools/train_net.py --eval-only --config-file configs/BAText/VinText/attn_R_50.yaml MODEL.WEIGHTS ./output/batext/vintext/trained_model.pth

Acknowledgement

This repository is built based-on ABCNet