igtr

IGTR

• 1. Introduction
• 2. Environment
• 3. Model Training / Evaluation
  • 3.1 Training
  • 3.2 Evaluation
  • 3.3 Prediction

1. Introduction

Paper:

Instruction-Guided Scene Text Recognition Yongkun Du, Zhineng Chen, Yuchen Su, Caiyan Jia, Yu-Gang Jiang

Multi-modal models show appealing performance in visual recognition tasks recently, as free-form text-guided training evokes the ability to understand fine-grained visual content. However, current models are either inefficient or cannot be trivially upgraded to scene text recognition (STR) due to the composition difference between natural and text images. We propose a novel instruction-guided scene text recognition (IGTR) paradigm that formulates STR as an instruction learning problem and understands text images by predicting character attributes, e.g., character frequency, position, etc. IGTR first devises $\left \langle condition,question,answer\right \rangle$ instruction triplets, providing rich and diverse descriptions of character attributes. To effectively learn these attributes through question-answering, IGTR develops lightweight instruction encoder, cross-modal feature fusion module and multi-task answer head, which guides nuanced text image understanding. Furthermore, IGTR realizes different recognition pipelines simply by using different instructions, enabling a character-understanding-based text reasoning paradigm that considerably differs from current methods. Experiments on English and Chinese benchmarks show that IGTR outperforms existing models by significant margins, while maintaining a small model size and efficient inference speed. Moreover, by adjusting the sampling of instructions, IGTR offers an elegant way to tackle the recognition of both rarely appearing and morphologically similar characters, which were previous challenges.

The accuracy (%) and model files of IGTR on the public dataset of scene text recognition are as follows:

• Trained on Synth dataset(MJ+ST), test on Common Benchmarks, training and test datasets both from PARSeq.

Model	IC13 857	SVT	IIIT5k 3000	IC15 1811	SVTP	CUTE80	Avg	Config&Model&Log
IGTR-PD	97.6	95.2	97.6	88.4	91.6	95.5	94.30	link
IGTR-AR	98.6	95.7	98.2	88.4	92.4	95.5	94.78	as above

• Test on Union14M-L benchmark, from Union14M.

Model	Curve	Multi- Oriented	Artistic	Contextless	Salient	Multi- word	General	Avg	Config&Model&Log
IGTR-PD	76.9	30.6	59.1	63.3	77.8	62.5	66.7	62.40	Same as the above table
IGTR-AR	78.4	31.9	61.3	66.5	80.2	69.3	67.9	65.07	as above

• Trained on Union14M-L training dataset.

Model	IC13 857	SVT	IIIT5k 3000	IC15 1811	SVTP	CUTE80	Avg	Config&Model&Log
IGTR-PD	97.7	97.7	98.3	89.8	93.7	97.9	95.86	link
IGTR-AR	98.1	98.4	98.7	90.5	94.9	98.3	96.48	as above
IGTR-PD-60ep	97.9	98.3	99.2	90.8	93.7	97.6	96.24	link
IGTR-AR-60ep	98.4	98.1	99.3	91.5	94.3	97.6	96.54	as above
IGTR-PD-PT	98.6	98.0	99.1	91.7	96.8	99.0	97.20	link
IGTR-AR-PT	98.8	98.3	99.2	92.0	96.8	99.0	97.34	as above

Model	Curve	Multi- Oriented	Artistic	Contextless	Salient	Multi- word	General	Avg	Config&Model&Log
IGTR-PD	88.1	89.9	74.2	80.3	82.8	79.2	83.0	82.51	Same as the above table
IGTR-AR	90.4	91.2	77.0	82.4	84.7	84.0	84.4	84.86	as above
IGTR-PD-60ep	90.0	92.1	77.5	82.8	86.0	83.0	84.8	85.18	Same as the above table
IGTR-AR-60ep	91.0	93.0	78.7	84.6	87.3	84.8	85.6	86.43	as above
IGTR-PD-PT	92.4	92.1	80.7	83.6	87.7	86.9	85.0	86.92	Same as the above table
IGTR-AR-PT	93.0	92.9	81.3	83.4	88.6	88.7	85.6	87.65	as above

• Trained and test on Chinese dataset, from Chinese Benckmark.

Model	Scene	Web	Document	Handwriting	Avg	Config&Model&Log
IGTR-PD	73.1	74.8	98.6	52.5	74.75
IGTR-AR	75.1	76.4	98.7	55.3	76.37
IGTR-PD-TS	73.5	75.9	98.7	54.5	75.65	link
IGTR-AR-TS	75.6	77.0	98.8	57.3	77.17	as above
IGTR-PD-Aug	79.5	80.0	99.4	58.9	79.45	link
IGTR-AR-Aug	82.0	81.7	99.5	63.8	81.74	as above

Download all Configs, Models, and Logs from Google Drive.

2. Environment

• PyTorch version >= 1.13.0
• Python version >= 3.7

git clone -b develop https://github.com/Topdu/OpenOCR.git
cd OpenOCR
# A100 Ubuntu 20.04 Cuda 11.8
conda create -n openocr python==3.8
conda activate openocr
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt

Dataset Preparation

English dataset download

Union14M-L download

Chinese dataset download

The expected filesystem structure is as follows:

benchmark_bctr
├── benchmark_bctr_test
│   ├── document_test
│   ├── handwriting_test
│   ├── scene_test
│   └── web_test
└── benchmark_bctr_train
    ├── document_train
    ├── handwriting_train
    ├── scene_train
    └── web_train
evaluation
├── CUTE80
├── IC13_857
├── IC15_1811
├── IIIT5k
├── SVT
└── SVTP
OpenOCR
synth
├── MJ
│   ├── test
│   ├── train
│   └── val
└── ST
test # from PARSeq
├── ArT
├── COCOv1.4
├── CUTE80
├── IC13_1015
├── IC13_1095  
├── IC13_857
├── IC15_1811
├── IC15_2077
├── IIIT5k
├── SVT
├── SVTP
└── Uber
u14m # lmdb format
├── artistic
├── contextless
├── curve
├── general
├── multi_oriented
├── multi_words
└── salient
Union14M-LMDB-L # lmdb format
├── train_challenging
├── train_easy
├── train_hard
├── train_medium
└── train_normal

3. Model Training / Evaluation

Training:

# The configuration file is available from the link provided in the table above.
# Multi GPU training
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 tools/train_rec.py --c PATH/svtr_base_igtr_XXX.yml

Evaluation:

# The configuration file is available from the link provided in the table above.
# en
python tools/eval_rec_all_ratio.py --c PATH/svtr_base_igtr_syn.yml
# ch
python tools/eval_rec_all_ch.py --c PATH/svtr_base_igtr_ch_aug.yml

引用

@article{Du2024IGTR,
  title     = {Instruction-Guided Scene Text Recognition},
  author    = {Du, Yongkun and Chen, Zhineng and Su, Yuchen and Jia, Caiyan and Jiang, Yu-Gang},
  journal   = {CoRR},
  eprinttype = {arXiv},
  primaryClass={cs.CV},
  volume    = {abs/2401.17851},
  year      = {2024},
  url       = {https://arxiv.org/abs/2401.17851}
}